Journal articles on the topic 'Adaptive mutation scheme'
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Bajer, Dražen. "Adaptive k-tournament mutation scheme for differential evolution." Applied Soft Computing 85 (December 2019): 105776. http://dx.doi.org/10.1016/j.asoc.2019.105776.
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Xiang, Fei, and Shan Li. "Parameter Optimization of PID Controller for Boiler Combustion System by Applying Adaptive Immune Genetic Algorithm." Advanced Materials Research 546-547 (July 2012): 961–66. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.961.
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For power plant boiler combustion control system has large inertia, nonlinear and other complex characteristics, a control algorithm of PID optimized by means of adaptive immune genetic algorithm is presented. A variety of improved schemes of GA were designed, include: initial population generating scheme, fitness function design scheme, immunization strategy, adaptive crossover probability and adaptive mutation probability design scheme. By taking the rise time, error integral and overshoot of system response as the performance index, and using genetic algorithm for real-coded of PID parameters, then a group of optimal values were obtained. Simulation results show that the method has a good dynamic performance, superior to the conventional PID controller.
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Stanovov, Vladimir, Shakhnaz Akhmedova, and Eugene Semenkin. "Dual-Population Adaptive Differential Evolution Algorithm L-NTADE." Mathematics 10, no. 24 (December 9, 2022): 4666. http://dx.doi.org/10.3390/math10244666.
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This study proposes a dual-population algorithmic scheme for differential evolution and specific mutation strategy. The first population contains the newest individuals, and is continuously updated, whereas the other keeps the top individuals throughout the whole search process. The proposed mutation strategy combines information from both populations. The proposed L-NTADE algorithm (Linear population size reduction Newest and Top Adaptive Differential Evolution) follows the L-SHADE approach by utilizing its parameter adaptation scheme and linear population size reduction. The L-NTADE is tested on two benchmark sets, namely CEC 2017 and CEC 2022, and demonstrates highly competitive results compared to the state-of-the-art methods. The deeper analysis of the results shows that it displays different properties compared to known DE schemes. The simplicity of L-NTADE coupled with its high efficiency make it a promising approach.
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Xu, Changqiao, Tao Zhang, Xiaohui Kuang, Zan Zhou, and Shui Yu. "Context-Aware Adaptive Route Mutation Scheme: A Reinforcement Learning Approach." IEEE Internet of Things Journal 8, no. 17 (September 1, 2021): 13528–41. http://dx.doi.org/10.1109/jiot.2021.3065680.
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Yang, Wen Xue, Zhe Chen, and Cheng Jun Li. "Adaptive Clone Selection Algorithm for Function Optimization." Applied Mechanics and Materials 644-650 (September 2014): 2147–50. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2147.
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In this paper, we present a scheme to improve immune cloning selection algorithm. The improved algorithm, which is referred to as adaptive cloning selection algorithm (ACSA), is proposed and then applied to function optimization. At first, we present adaptive gene mutation which decides mutation probability of each code point (locus) based on the quality of antibodies and the number of evolution iterations. Secondly, we present an iteratively increasing method from one locus to the all ones, which can be used in function optimization. Then, the cloning selection process of evolution is divided into two-stages. The first step is to increase locus of antibodies. In the other one, the Baldwin effect learning operator is employed. And finally, an experiment is carried out to verify the theoretical analyses on several testing functions.
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Xu, DongHui, JingYuan He, Qiang Bian, SiYi Liu, and JiangLiang Liu. "Research on Relay Network Method of Aerial Platform." Journal of Physics: Conference Series 2480, no. 1 (April 1, 2023): 012016. http://dx.doi.org/10.1088/1742-6596/2480/1/012016.
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Abstract As a regional wireless communication means, a wireless broadband communication system mainly provides communication services for mountainous areas with imperfect infrastructure construction. When the system provides communication services in mountainous areas, the vehicle-mounted base stations encounter obstacles to block communication, which causes the problem of non-cascade communication. In this paper, an improved genetic algorithm based on the adaptive change of crossover and mutation probability is proposed to formulate the air platform relay network planning scheme. The simulation results show that the crossover and mutation probabilities of the adaptive improved genetic algorithm change with the iteration of the algorithm, which improves the search ability of the algorithm and prevents it from falling into the local optimal solution.
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Dawar, Deepak, and Simone A. Ludwig. "Effect of Strategy Adaptation on Differential Evolution in Presence and Absence of Parameter Adaptation: An Investigation." Journal of Artificial Intelligence and Soft Computing Research 8, no. 3 (July 1, 2018): 211–35. http://dx.doi.org/10.1515/jaiscr-2018-0014.
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AbstractDifferential Evolution (DE) is a simple, yet highly competitive real parameter optimizer in the family of evolutionary algorithms. A significant contribution of its robust performance is attributed to its control parameters, and mutation strategy employed, proper settings of which, generally lead to good solutions. Finding the best parameters for a given problem through the trial and error method is time consuming, and sometimes impractical. This calls for the development of adaptive parameter control mechanisms. In this work, we investigate the impact and efficacy of adapting mutation strategies with or without adapting the control parameters, and report the plausibility of this scheme. Backed with empirical evidence from this and previous works, we first build a case for strategy adaptation in the presence as well as in the absence of parameter adaptation. Afterwards, we propose a new mutation strategy, and an adaptive variant SA-SHADE which is based on a recently proposed self-adaptive memory based variant of Differential evolution, SHADE. We report the performance of SA-SHADE on 28 benchmark functions of varying complexity, and compare it with the classic DE algorithm (DE/Rand/1/bin), and other state-of-the-art adaptive DE variants including CoDE, EPSDE, JADE, and SHADE itself. Our results show that adaptation of mutation strategy improves the performance of DE in both presence, and absence of control parameter adaptation, and should thus be employed frequently.
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Ye, Fang, Jie Chen, Yuan Tian, and Tao Jiang. "Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm." Electronics 9, no. 4 (April 23, 2020): 687. http://dx.doi.org/10.3390/electronics9040687.
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The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.
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Wong, Ieong, Wenjia Liu, Chih-Ming Ho, and Xianting Ding. "Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization." SLAS TECHNOLOGY: Translating Life Sciences Innovation 22, no. 3 (January 31, 2017): 289–305. http://dx.doi.org/10.1177/2472630317690318.
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Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction–based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.
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Yang, Jie, Haotian Zhu, Junxu Ma, Bin Yue, Yang Guan, Jinfa Shi, and Linjian Shangguan. "Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks." Applied Sciences 13, no. 16 (August 15, 2023): 9256. http://dx.doi.org/10.3390/app13169256.
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In this paper, for the problem of high total fuel consumption of distribution trucks when multiple concrete-mixing plants distribute concrete together, we established a green fuel consumption model for distribution trucks and solved the model with an improved genetic algorithm to obtain a green distribution scheme for trucks. Firstly, the fuel consumption model is established for the characteristics of commercial concrete tankers; secondly, the adaptive elite retention strategy, adaptive crossover, mutation operator, and immune operation are added to the genetic algorithm to improve it; and finally, the model is solved to obtain the green distribution scheme. The total fuel consumption in this experiment was 189.6 L when the green distribution scheme was used; compared to the total fuel consumption under the original scheme (240 L), the total fuel consumption was reduced by 21.25%. The experimental results show that the total fuel consumption of delivery trucks can be significantly reduced based on the established green fuel consumption model, and the improved genetic algorithm can effectively solve the model.
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Farda, Irfan, and Arit Thammano. "An Adaptive Differential Evolution with Multiple Crossover Strategies for Optimization Problems." HighTech and Innovation Journal 5, no. 2 (June 1, 2024): 231–58. http://dx.doi.org/10.28991/hij-2024-05-02-02.
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The efficiency of a Differential Evolution (DE) algorithm largely depends on the control parameters of the mutation strategy. However, fixed-value control parameters are not effective for all types of optimization problems. Furthermore, DE search capability is often restricted, leading to limited exploration and poor exploitation when relying on a single strategy. These limitations cause DE algorithms to potentially miss promising regions, converge slowly, and stagnate in local optima. To address these drawbacks, we proposed a new Adaptive Differential Evolution Algorithm with Multiple Crossover Strategy Scheme (ADEMCS). We introduced an adaptive mutation strategy that enabled DE to adapt to specific optimization problems. Additionally, we augmented DE with a powerful local search ability: a hunting coordination operator from the reptile search algorithm for faster convergence. To validate ADEMCS effectiveness, we ran extensive experiments using 32 benchmark functions from CEC2015 and CEC2016. Our new algorithm outperformed nine state-of-the-art DE variants in terms of solution quality. The integration of the adaptive mutation strategy and the hunting coordination operator significantly enhanced DE's global and local search capabilities. Overall, ADEMCS represented a promising approach for optimization, offering adaptability and improved performance over existing variants. Doi: 10.28991/HIJ-2024-05-02-02 Full Text: PDF
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Zhuo, Xin Jian, and Han Wen Wang. "Self-Adaptive Particle Swarm Mutation Rate of Genetic Algorithm in the Link Optimization of Network Coding." Applied Mechanics and Materials 713-715 (January 2015): 2478–81. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2478.
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Considering a multicast scenario, we want to minimize the coding links used for network coding while achieving the desired throughput. This article solves the optimization of the network coding to get with the lowest coding link scheme by using APGA. According to the simulation results, it can be concluded that the better performance of APGA than the previous algorithms.
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Gao, Shang, Chang Liu, and Guan Wang. "Multi-objective Green Flexible Workshop Scheduling Considering Multi-energy Consumption Factors." Journal of Physics: Conference Series 2174, no. 1 (January 1, 2022): 012083. http://dx.doi.org/10.1088/1742-6596/2174/1/012083.
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Abstract In order to solve the energy consumption problem of shop floor caused by machine processing, adjustment, idling and workpiece transportation, the green flexible workshop scheduling problem was studied, and a multi-objective optimization model was established with the maximum completion time, total machine load and total energy consumption as the objectives. The double-layer coding strategy was adopted to encode processes and machines respectively to optimize the scheduling scheme, and then the adaptive hybrid crossover scheme, mutation scheme and improved elite retention strategy were adopted to improve the operation efficiency and population diversity of NSGA-II algorithm. Finally, a case study is used to verify the effectiveness of the algorithm in solving the multi-objective green flexible workshop scheduling problem, which provides a reference for enterprises to achieve green manufacturing.
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Zhao, Fuqing, Wenchang Lei, Weimin Ma, Yang Liu, and Chuck Zhang. "An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems." Mathematical Problems in Engineering 2016 (2016): 1–20. http://dx.doi.org/10.1155/2016/8010346.
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A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA) is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.
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Mohamed, Ali Wagdy, and Abdulaziz S. Almazyad. "Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/7974218.
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This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.
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Zhang, Haijun, Qiong Yan, Yuanpeng Liu, and Zhiqiang Jiang. "An integer-coded differential evolution algorithm for simple assembly line balancing problem of type 2." Assembly Automation 36, no. 3 (August 1, 2016): 246–61. http://dx.doi.org/10.1108/aa-11-2015-089.
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Purpose This paper aims to develop a new differential evolution algorithm (DEA) for solving the simple assembly line balancing problem of type 2 (SALBP-2). Design/methodology/approach Novel approaches of mutation operator and crossover operator are presented. A self-adaptive double mutation scheme is implemented and an elitist strategy is used in the selection operator. Findings Test and comparison results show that the proposed IDEA obtains better results for SALBP-2. Originality/value The presented DEA is called the integer-coded differential evolution algorithm (IDEA), which can directly deal with integer variables of SALBP-2 on a discrete space without any posterior conversion. The proposed IDEA will be an alternative in evolutionary algorithms, especially for various integer/discrete-valued optimization problems.
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Ma, Xunde, Li Bi, Xiaogang Jiao, and Junjie Wang. "An Efficient and Improved Coronavirus Herd Immunity Algorithm Using Knowledge-Driven Variable Neighborhood Search for Flexible Job-Shop Scheduling Problems." Processes 11, no. 6 (June 15, 2023): 1826. http://dx.doi.org/10.3390/pr11061826.
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By addressing the flexible job shop scheduling problem (FJSP), this paper proposes a new type of algorithm for the FJSP. We named it the hybrid coronavirus population immunity optimization algorithm. Based on the characteristics of the problem, firstly, this paper redefined the discretized two-stage individual encoding and decoding scheme. Secondly, in order to realize the multi-scale search of the solution space, a multi-population update mechanism is designed, and a collaborative learning method is proposed to ensure the diversity of the population. Then, an adaptive mutation operation is introduced to enrich the diversity of the population, relying on the adaptive adjustment of the mutation operator to balance global search and local search capabilities. In order to realize a directional and efficient neighborhood search, this algorithm proposed a knowledge-driven variable neighborhood search strategy. Finally, the algorithm’s performance comparison experiment is carried out. The minimum makespans on the MK06 medium-scale case and MK10 large-scale case are 58 and 201, respectively. The experimental results verify the effectiveness of the hybrid algorithm.
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Zhou, Xiaoyi, Chunjie Cao, Jixin Ma, and Longjuan Wang. "Adaptive Digital Watermarking Scheme Based on Support Vector Machines and Optimized Genetic Algorithm." Mathematical Problems in Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2685739.
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Digital watermarking is an effective solution to the problem of copyright protection, thus maintaining the security of digital products in the network. An improved scheme to increase the robustness of embedded information on the basis of discrete cosine transform (DCT) domain is proposed in this study. The embedding process consisted of two main procedures. Firstly, the embedding intensity with support vector machines (SVMs) was adaptively strengthened by training 1600 image blocks which are of different texture and luminance. Secondly, the embedding position with the optimized genetic algorithm (GA) was selected. To optimize GA, the best individual in the first place of each generation directly went into the next generation, and the best individual in the second position participated in the crossover and the mutation process. The transparency reaches 40.5 when GA’s generation number is 200. A case study was conducted on a 256 × 256 standard Lena image with the proposed method. After various attacks (such as cropping, JPEG compression, Gaussian low-pass filtering (3,0.5), histogram equalization, and contrast increasing (0.5,0.6)) on the watermarked image, the extracted watermark was compared with the original one. Results demonstrate that the watermark can be effectively recovered after these attacks. Even though the algorithm is weak against rotation attacks, it provides high quality in imperceptibility and robustness and hence it is a successful candidate for implementing novel image watermarking scheme meeting real timelines.
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Wen-jing, Wang. "Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities." International Journal of Emerging Technologies in Learning (iJET) 13, no. 06 (May 29, 2018): 29. http://dx.doi.org/10.3991/ijet.v13i06.8442.
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Traditional artificial intelligence and computer-aided course scheduling schemes can no longer meet the increasing demands caused by the informatization of teaching management in colleges and universities. To address this problem, this study designed an improved adaptive genetic algorithm that is based on hard and soft constraints for course scheduling. First, the mathematical model of the genetic algorithm was established. The combination of time, teacher, and course number was regarded as the gene coding. The weekly course schedule of each class was a chromosome, and the course schedule of the entire school was the initial population. The fitness was designed according to the priority of each class, curriculum dispersion, and teacher satisfaction. Local columns between individuals were selected through the roulette principle for a variation of crossover and random columns. Iterative calculation was implemented on the basis of the default mutation and crossover rates to study the optimal course scheduling scheme. Experimental results demonstrate that the improved adaptive genetic algorithm is superior to the original genetic algorithm. When the number of iterations is 150, population evolution is optimal and the fitness does not increase. When the population size is 150 classes, the average scheduling time is the shortest. The basic, adaptive, and improved adaptive genetic algorithms are compared in terms of the number of average iterations required for convergence, maximum individual fitness, and average individual fitness. Comparison results show that the improved adaptive genetic algorithm is superior to the two other algorithms. This study provides references for the model building and evaluation of course scheduling in colleges and universities.
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Djebar, Hocine, Abdelatif Bencherif-Madani, Raouf Ziadi, and Choubeila Souli. "DJADE: a reliable and efficient adaptive differential evolution algorithm with selection pressure control." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 1 (June 11, 2024): 2705–31. http://dx.doi.org/10.54021/seesv5n1-134.
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Our main contribution is the proposition of a new and explicit way of adapting the parental selection pressure for Differential Evolution algorithm (DE) and its experimentation on an adaptive version of differential evolution algorithm named JADE in order to improve its reliability without diminishing its efficiency. The new algorithm named DJADE employs a genotypic population diversity measure (a measure of population convergence) to control the selection pressure parameter. A mechanism that increases the parental selection pressure as the search progresses and in the same time serves as a mean to detect when the algorithm begins to converge (the "convergence phase") is presented. In addition, DJADE is a novel adaptive DE, which adapts crossover, mutation and selection pressure parameters. Moreover, the coefficient K of greediness of the mutation scheme DE/current-to- best/ is also adapted. The motivation for the parameter adaptation is to obtain an algorithm that is capable to handle various problems with different characteristics, notably difficult multimodal and/or nonseparable functions. No case of premature convergence is observed during the experiments conducted on 13 classical functions test at 05 and 30 dimensions. The comparison with classical JADE and Classical DE are presented. DJADE is not only very efficient but also reliable on all functions tested.
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Adams, Christopher J., Mitchell Conery, Benjamin J. Auerbach, Shane T. Jensen, Iain Mathieson, and Benjamin F. Voight. "Regularized sequence-context mutational trees capture variation in mutation rates across the human genome." PLOS Genetics 19, no. 7 (July 7, 2023): e1010807. http://dx.doi.org/10.1371/journal.pgen.1010807.
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Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics inference methods. Previous models have demonstrated that nucleotides flanking polymorphic sites–the local sequence context–explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways–first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data.
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GONG, WENYIN, ZHIHUA CAI, LIYUAN JIA, and HUI LI. "A GENERALIZED HYBRID GENERATION SCHEME OF DIFFERENTIAL EVOLUTION FOR GLOBAL NUMERICAL OPTIMIZATION." International Journal of Computational Intelligence and Applications 10, no. 01 (March 2011): 35–65. http://dx.doi.org/10.1142/s1469026811002982.
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Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization over continuous domain, which has been widely used in many areas. Although DE is good at exploring the search space, it is slow at the exploitation of the solutions. To alleviate this drawback, in this paper, we propose a generalized hybrid generation scheme, which attempts to enhance the exploitation and accelerate the convergence velocity of the original DE algorithm. In the hybrid generation scheme the operator with powerful exploitation is hybridized with the original DE operator. In addition, a self-adaptive exploitation factor is introduced to control the frequency of the exploitation operation. In order to evaluate the performance of our proposed generation scheme, two operators, the migration operator of biogeography-based optimization and the "DE/best/1" mutation operator, are employed as the exploitation operator. Moreover, 23 benchmark functions (including 10 test functions provided by CEC2005 special session) are chosen from the literature as the test suite. Experimental results confirm that the new hybrid generation scheme is able to enhance the exploitation of the original DE algorithm and speed up its convergence rate.
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Kang Kim, Hojin, Raimundo Becerra, Sandy Bolufé, Cesar A. Azurdia-Meza, Samuel Montejo-Sánchez, and David Zabala-Blanco. "Neuroevolution-Based Adaptive Antenna Array Beamforming Scheme to Improve the V2V Communication Performance at Intersections." Sensors 21, no. 9 (April 23, 2021): 2956. http://dx.doi.org/10.3390/s21092956.
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The opportunistic exchange of information between vehicles can significantly contribute to reducing the occurrence of accidents and mitigating their damages. However, in urban environments, especially at intersection scenarios, obstacles such as buildings and walls block the line of sight between the transmitter and receiver, reducing the vehicular communication range and thus harming the performance of road safety applications. Furthermore, the sizes of the surrounding vehicles and weather conditions may affect the communication. This makes communications in urban V2V communication scenarios extremely difficult. Since the late notification of vehicles or incidents can lead to the loss of human lives, this paper focuses on improving urban vehicle-to-vehicle (V2V) communications at intersections by using a transmission scheme able of adapting to the surrounding environment. Therefore, we proposed a neuroevolution of augmenting topologies-based adaptive beamforming scheme to control the radiation pattern of an antenna array and thus mitigate the effects generated by shadowing in urban V2V communication at intersection scenarios. This work considered the IEEE 802.11p standard for the physical layer of the vehicular communication link. The results show that our proposal outperformed the isotropic antenna in terms of the communication range and response time, as well as other traditional machine learning approaches, such as genetic algorithms and mutation strategy-based particle swarm optimization.
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Su, Jian Jiang, Chao Che, Qiang Zhang, and Xiao Peng Wei. "Satellite Module Layout Design Based on Adaptive Bee Evolutionary Genetic Algorithm." Applied Mechanics and Materials 538 (April 2014): 193–97. http://dx.doi.org/10.4028/www.scientific.net/amm.538.193.
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The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.
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Ho-Huu, V., T. Vo-Duy, T. Luu-Van, L. Le-Anh, and T. Nguyen-Thoi. "Optimal design of truss structures with frequency constraints using improved differential evolution algorithm based on an adaptive mutation scheme." Automation in Construction 68 (August 2016): 81–94. http://dx.doi.org/10.1016/j.autcon.2016.05.004.
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Zhang, Zhu Hong. "Noisy Immune Optimization for Chance-Constrained Programming Problems." Applied Mechanics and Materials 48-49 (February 2011): 740–44. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.740.
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This work puts forward a parameter-less and practical immune optimization mechanism in noisy environments to deal with single-objective chance-constrained programming problems without prior noisy information. In this practical mechanism, an adaptive sampling scheme and a new concept of reliability-dominance are established to evaluate individuals, while three immune operators borrowed from several simplified immune metaphors in the immune system and the idea of fitness inheritance are utilized to evolve the current population, in order to weaken noisy influence to the optimized quality. Under the mechanism, three kinds of algorithms are obtained through changing its mutation rule. Experimental results show that the mechanism can achieve satisfactory performances including the quality of optimization, noise compensation and performance efficiency.
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Wang, Lin Yuan. "Research on Reactive Power Optimization Based on Genetic Algorithms in Distribution Network." Applied Mechanics and Materials 644-650 (September 2014): 2476–78. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2476.
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The reactive power optimization is formulated based on genetic algorithms in distribution net work. SGA has defects of slow convergence and being prone to immature convergence. In order to eliminate the defects, an improved GA is proposed in this thesis. CIP scheme is presented, which can guarantee diversity of the population by designing the initial population to obtain all the values within the definition area. A parameter called individual distributing degree is defined to describe how individuals are distributed in the definition area. Adaptive mutation rate is defined as an exponential function of the retained generations of the Elitism, and it is in inverse proportion to individual distribution degree. It accelerates the convergent process.
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Yao, Jin Jie, Xiang Ju, Li Ming Wang, Jin Xiao Pan, and Yan Han. "Training Back-Propagation Neural Network for Target Localization Using Improved Particle Swarm Optimization." Applied Mechanics and Materials 333-335 (July 2013): 1384–87. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1384.
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Target localization technology has been intensively studied and broadly applied in many fields. This paper presents one improved particle swarm optimization technique in training a back-propagation neural network for position estimation in target localization. The proposed scheme combines particle swarm optimization (PSO), back-propagation neural network (BP), adaptive inertia weight and hybrid mutation, called IPSO-BP. To verify the proposed IPSO-BP approach, comparisons between the PSO-based BP approach (PSO-BP) and general back-propagation neural network (BP) are made. The computational results show that the proposed IPSO-BP approach exhibits much better performance in the training process and better prediction ability in the validation process than those using the other two base line approaches.
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Lin, Wenliang, Zewen Dong, Ke Wang, Dongdong Wang, Yaohua Deng, Yicheng Liao, Yang Liu, Da Wan, Bingyu Xu, and Genan Wu. "A Novel Load Balancing Scheme for Satellite IoT Networks Based on Spatial–Temporal Distribution of Users and Advanced Genetic Algorithms." Sensors 22, no. 20 (October 18, 2022): 7930. http://dx.doi.org/10.3390/s22207930.
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Satellite IoT networks (S-IoT-N), which have been a hot issue regarding the next generation of communication, are quite important for the coming era of digital twins and the metaverse because of their performance in sensing and monitoring anywhere, anytime, and anyway, in more dimensions. However, this will cause communication links to face greater traffic loads. Satellite internet networks (SIN) are considered the most possible evolution road, possessing characteristics of many satellites, such as low earth orbit (LEO), the Ku/Ka frequency, and a high data rate. Existing research on load balancing schemes for satellite networks cannot solve the problems of low efficiency under conditions of extremely non-uniform distribution of users (DoU) and dynamic density variances. Therefore, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial–temporal DoU and advanced GA. In our scheme, the PDF of the DoU in the direction of movement of the SSP’s trajectory was modeled first, which provided a multi-directional constraint for the non-uniform distribution of users in S-IoT-N. Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we proposed an adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. Based on the proposed improved GA, we obtained the optimal scheme of load balancing under the conditions of the adaptation from the local balancing scheme to global balancing, and a selection of Ser-Beams to access. Finally, the simulations show that the proposed method can improve the average throughput by 3% under specific conditions and improve processing efficiency by 30% on average.
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Tariq, Hasnat Bin, Naveed Ishtiaq Chaudhary, Zeshan Aslam Khan, Muhammad Asif Zahoor Raja, Khalid Mehmood Cheema, and Ahmad H. Milyani. "Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification." Mathematics 9, no. 24 (December 11, 2021): 3199. http://dx.doi.org/10.3390/math9243199.
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Most real-time systems are nonlinear in nature, and their optimization is very difficult due to inherit stiffness and complex system representation. The computational intelligent algorithms of evolutionary computing paradigm (ECP) effectively solve various complex, nonlinear optimization problems. The differential evolution algorithm (DEA) is one of the most important approaches in ECP, which outperforms other standard approaches in terms of accuracy and convergence performance. In this study, a novel application of a recently proposed variant of DEA, the so-called, maximum-likelihood-based, adaptive, differential evolution algorithm (ADEA), is investigated for the identification of nonlinear Hammerstein output error (HOE) systems that are widely used to model different nonlinear processes of engineering and applied sciences. The performance of the ADEA is evaluated by taking polynomial- and sigmoidal-type nonlinearities in two case studies of HOE systems. Moreover, the robustness of the proposed scheme is examined for different noise levels. Reliability and consistent accuracy are assessed through multiple independent trials of the scheme. The convergence, accuracy, robustness and reliability of the ADEA are carefully examined for HOE identification in comparison with the standard counterpart of the DEA. The ADEA achieves the fitness values of 1.43 × 10−8 and 3.46 × 10−9 for a population size of 80 and 100, respectively, in the HOE system identification problem of case study 1 for a 0.01 nose level, while the respective fitness values in the case of DEA are 1.43 × 10−6 and 3.46 × 10−7. The ADEA is more statistically consistent but less complex when compared to the DEA due to the extra operations involved in introducing the adaptiveness during the mutation and crossover. The current study may consider the approach of effective nonlinear system identification as a step further in developing ECP-based computational intelligence.
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Lei, Weidong, Hervé Manier, Marié-Ange Manier, and Xinping Wang. "A Hybrid Quantum Evolutionary Algorithm with Improved Decoding Scheme for a Robotic Flow Shop Scheduling Problem." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/3064724.
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We aim at solving the cyclic scheduling problem with a single robot and flexible processing times in a robotic flow shop, which is a well-known optimization problem in advanced manufacturing systems. The objective of the problem is to find an optimal robot move sequence such that the throughput rate is maximized. We propose a hybrid algorithm based on the Quantum-Inspired Evolutionary Algorithm (QEA) and genetic operators for solving the problem. The algorithm integrates three different decoding strategies to convert quantum individuals into robot move sequences. The Q-gate is applied to update the states of Q-bits in each individual. Besides, crossover and mutation operators with adaptive probabilities are used to increase the population diversity. A repairing procedure is proposed to deal with infeasible individuals. Comparison results on both benchmark and randomly generated instances demonstrate that the proposed algorithm is more effective in solving the studied problem in terms of solution quality and computational time.
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Long-Yan Xu. "Application of Improved Sparrow Search Algorithm to Flexible Job Shop Scheduling Problem." Journal of Electrical Systems 20, no. 7s (May 3, 2024): 424–35. http://dx.doi.org/10.52783/jes.3334.
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When the reality of energy saving and reduction of enterprises’ emissions are a concern, research on the investigation of establishing mathematical models to optimize the production time has been studied. In this article, an enhanced sparrow optimization method is proposed. First, two-layer coding is employed for workpieces and machines according to the model requirements. Secondly, the three-dimensional chaotic mapping scheme is presented to improve the population heterogeneity of the algorithm, and the adaptive inertia weight balance algorithm is implemented to offset the speed of the convergence and its probability. Finally, the Cauchy mutation scheme is adopted to help the algorithm jump out of the local optimum. Simulated data is run to check the superiority of the proposed method. So, through the simulations and comparisons of 10 kinds of test datasets, the outcomes suggest that the solution quality of the enhanced sparrow optimization method has been effectively advanced, and its good global optimization ability is shown, which can provide scheduling strategies for workshop productions. One of the successes of the ISSA algorithm is its superior search accuracy.
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Wang, Ling, Wei Ye, Haikuan Wang, Xiping Fu, Minrui Fei, and Ilyas Menhas. "Optimal node placement of industrial wireless sensor networks based on adaptive mutation probability binary particle swarm optimization algorithm." Computer Science and Information Systems 9, no. 4 (2012): 1553–76. http://dx.doi.org/10.2298/csis120117058w.
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Industrial Wireless Sensor Networks (IWSNs), a novel technique in industry control, can greatly reduce the cost of measurement and control and improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial applications, the communication reliability of IWSNs has to be guaranteed as the real-time field data need to be transmitted to the control system through IWSNs. Obviously, the network architecture has a significant influence on the performance of IWSNs, and therefore this paper investigates the optimal node placement problem of IWSNs to ensure the network reliability and reduce the cost. To solve this problem, a node placement model of IWSNs is developed and formulized in which the reliability, the setup cost, the maintenance cost and the scalability of the system are taken into account. Then an improved adaptive mutation probability binary particle swarm optimization algorithm (AMPBPSO) is proposed for searching out the best placement scheme. After the verification of the model and optimization algorithm on the benchmark problem, the presented AMPBPSO and the optimization model are used to solve various large-scale optimal sensor placement problems. The experimental results show that AMPBPSO is effective to tackle IWSNs node placement problems and outperforms discrete binary Particle Swarm Optimization (DBPSO) and standard Genetic Algorithm (GA) in terms of search accuracy and the convergence speed with the guaranteed network reliability.
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Guo, Wei, Lanju Kong, Xudong Lu, and Lizhen Cui. "An Intelligent Genetic Scheme for Multi-Objective Collaboration Services Scheduling." Symmetry 14, no. 10 (September 29, 2022): 2037. http://dx.doi.org/10.3390/sym14102037.
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The optimization of collaborative service scheduling is the main bottleneck restricting the efficiency and cost of collaborative service execution. It is helpful to reduce the cost and improve the efficiency to deal with the scheduling problem correctly and effectively. The traditional genetic algorithm can solve the multi-objective problem more comprehensively than the optimization algorithm, such as stochastic greedy algorithm. But in the actual situation, the traditional algorithm is still one-sided. The intelligent genetic scheme (IGS) proposed in this paper enhances the expansibility and diversity of the algorithm on the basis of traditional genetic algorithm. In the process of initial population selection, the initial population generation strategy is changed, a part of the population is randomly generated and the selection process is iteratively optimized, which is a selection method based on population asymmetric exchange to realize selection. Mutation factors enhance the diversity of the population in the adaptive selection based on individual innate quality. The proposed IGS can not only maintain individual diversity, increase the probability of excellent individuals, accelerate the convergence rate, but also will not lead to the ultimate result of the local optimal solution. It has certain advantages in solving the optimization problem, and provides a new idea and method for solving the collaborative service optimization scheduling problem, which can save manpower and significantly reduce costs on the premise of ensuring the quality. The experimental results show that Intelligent Genetic algorithm (IGS) not only has better scalability and diversity, but also can increase the probability of excellent individuals and accelerate the convergence speed.
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Weng, Jiaxuan, Yiran Liu, and Jian Wang. "A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction." Remote Sensing 15, no. 12 (June 6, 2023): 2953. http://dx.doi.org/10.3390/rs15122953.
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In order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combines the international reference ionosphere (IRI), statistical machine learning (SML), BP-NN, and MMAdapGA. Compared with the IRI, SML-based, and other neural network models, MMAdapGA-BP-NN has higher accuracy and a more stable prediction effect. Taking the Athens station in Greece as an example, the root mean square errors (RMSEs) of MMAdapGA-BP-NN in 2015 and 2020 are 2.84TECU and 0.85TECU, respectively, 52.27% and 72.13% lower than the IRI model. Compared with the single neural network model, the MMAdapGA-BP-NN model reduced RMSE by 28.82% and 24.11% in 2015 and 2020, respectively. Furthermore, compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has fewer iterations ranging from 10 to 30. The results show that the prediction effect and stability of the proposed model have obvious advantages. As a result, the model could be extended to an alternative prediction scheme for more ionospheric parameters.
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Tong, Dawei, Haifeng Wu, Changxin Liu, Zhangchao Guo, and Pei Li. "A Hybrid Prediction Model for Local Resistance Coefficient of Water Transmission Tunnel Maintenance Ventilation Based on Machine Learning." Applied Sciences 13, no. 16 (August 10, 2023): 9135. http://dx.doi.org/10.3390/app13169135.
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Multiple ducts in the working shaft and main body of tunnels form a combined tee structure. An efficient and accurate prediction method for the local resistance coefficient is the key to the design and optimization of the maintenance ventilation scheme. However, most existing studies use numerical simulations and model experiments to analyze the local resistance characteristics of specific structures and calculate the local resistance coefficient under specific ventilation conditions. Therefore, there are shortcomings of low efficiency and high cost in the ventilation scheme optimization when considering the influence of the local resistance. This paper proposes a hybrid prediction model for the local resistance coefficient of water transmission tunnel maintenance ventilation based on machine learning. The hybrid prediction model introduces the hybrid kernel into a relevance vector machine to build the hybrid kernel relevance vector machine model (HKRVM). The improved artificial jellyfish search algorithm (IAJS), which utilizes Fuch chaotic mapping, lens-imaging reverse learning, and adaptive hybrid mutation strategies to improve the algorithm performance, is applied to the kernel parameter optimization of the HKRVM model. The results of a case study show that the method proposed in this paper can achieve the efficient and accurate prediction of the local resistance coefficient of maintenance ventilation and improve the prediction accuracy and prediction efficiency to a certain extent. The method proposed in this paper provides a new concept for the prediction of the ventilation local resistance coefficient and can further provide an efficient prediction method for the design and optimization of maintenance ventilation schemes.
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Sun, Enchang, Hanxing Qu, Yongyi Yuan, Meng Li, Zhuwei Wang, and Dawei Chen. "A Joint Channel Allocation and Power Control Scheme for D2D Communication in UAV-Based Networks." Wireless Communications and Mobile Computing 2021 (October 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/7400156.
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With the increasing application of unmanned aerial vehicles (UAVs), UAV-based base stations (BSs) have been widely used. In some situations when there is no ground BSs, such as mountainous areas and isolated islands, or BSs being out of service, like disaster areas, UAV-based networks may be rapidly deployed. In this paper, we propose a framework for UAV deployment, power control, and channel allocation for device-to-device (D2D) users, which is used for the underlying D2D communication in UAV-based networks. Firstly, the number and location of UAVs are iteratively optimized by the particle swarm optimization- (PSO-) Kmeans algorithm. After UAV deployment, this study maximizes the energy efficiency (EE) of D2D pairs while ensuring the quality of service (QoS). To solve this optimization problem, the adaptive mutation salp swarm algorithm (AMSSA) is proposed, which adopts the population variation strategy, the dynamic leader-follower numbers, and position update, as well as Q -learning strategy. Finally, simulation results show that the PSO-Kmeans algorithm can achieve better communication quality of cellular users (CUEs) with fewer UAVs compared with the PSO algorithm. The AMSSA has excellent global searching ability and local mining ability, which is not only superior to other benchmark schemes but also closer to the optimal performance of D2D pairs in terms of EE.
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Rustamov, G. A., and R. G. Rustamov. "Problems of Setting Robust Control Systems." Mekhatronika, Avtomatizatsiya, Upravlenie 23, no. 8 (August 12, 2022): 406–13. http://dx.doi.org/10.17587/mau.23.406-413.
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The issues of expediency of using adaptation tools in robust control systems are discussed. It is stated that purely robust systems without the use of adaptation tools, in principle, cannot provide high efficiency and versatility in solving practical problems. Robust control systems were originally conceived as non-adaptive systems (passive adaptive systems). In view of the emerging problems in solving practical problems, then a mutation occurred and works appeared under the name "Adaptive-robust systems", "Combined robust systems", etc., partially using adaptation algorithms. Setting the problem of control synthesis under conditions of uncertainty without elements of adaptation is figuratively speaking similar to "search for a black cat in a dark room, especially if it is not there" (Confucius). The most adequate from the point of view of compliance with the fundamental principles of the theory of automatic control is an approach based on an increase in the gain of an open loop. Nevertheless, here too a problem arises — an increase in the gain violates the stability of a closed system. All known research is concentrated around the solution of this problem. An integrated gain self-tuning algorithm and the corresponding circuitry and Simulink implementation scheme have been developed. The reliability of theoretical reasoning was verified by simulating a limiting robust system with self-tuning and a parametrically indeterminate object — "peak gyroscope (parametric pendulum)". Computer studies have made it possible to draw a number of positive conclusions that are of great practical importance.
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Ding, Bowen, Zhaobin Ma, Shuoyan Ren, Yi Gu, Pengjiang Qian, and Xin Zhang. "A genetic algorithm with two-step rank-based encoding for closed-loop supply chain network design." Mathematical Biosciences and Engineering 19, no. 6 (2022): 5925–56. http://dx.doi.org/10.3934/mbe.2022277.
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<abstract> <p>The closed-loop supply chain (CLSC) plays an important role in sustainable development and can help to increase the economic benefits of enterprises. The optimization for the CLSC network is a complicated problem, since it often has a large problem scale and involves multiple constraints. This paper proposes a general CLSC model to maximize the profits of enterprises by determining the transportation route and delivery volume. Due to the complexity of the multi-constrained and large-scale model, a genetic algorithm with two-step rank-based encoding (GA-TRE) is developed to solve the problem. Firstly, a two-step rank-based encoding is designed to handle the constraints and increase the algorithm efficiency, and the encoding scheme is also used to improve the genetic operators, including crossover and mutation. The first step of encoding is to plan the routes and predict their feasibility according to relevant constraints, and the second step is to set the delivery volume based on the feasible routes using a rank-based method to achieve greedy solutions. Besides, a new mutation operator and an adaptive population disturbance mechanism are designed to increase the diversity of the population. To validate the efficiency of the proposed algorithm, six heuristic algorithms are compared with GA-TRE by using different instances with three problem scales. The results show that GA-TRE can obtain better solutions than the competitors, especially on large-scale instances.</p> </abstract>
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Cong, Sun, and Cao Liang. "Research on trajectory tracking control of 7-DOF picking manipulator." Science Progress 104, no. 1 (January 2021): 003685042110033. http://dx.doi.org/10.1177/00368504211003383.
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In order to solve the problem of poor robustness of the traditional method of calculating torque in the mechanical model of 7-DOF picking manipulator, this paper proposes a control strategy of calculating torque plus fuzzy compensation by using adaptive fuzzy logic system to compensate the uncertain part of the mechanical model of 7-DOF picking manipulator. By using Lagrange method, the dynamic model of 7-DOF manipulator is established, and the relationship between joint motion and applied torque (force) is obtained. Using ADAMS and MATLAB to establish a co-simulation platform, the manipulator and trajectory tracking control system are simulated. The results show that the trajectory tracking error of each joint in the algorithm is obviously reduced and the convergence trend is obvious. The average trajectory tracking accuracy of joint 1 to joint 7 was improved by 70.22%, 94.78%, 0.62%, 74.23%, 89.78%, 86.45%, and 67.15%, respectively. In this control scheme, the control force (moment) of each joint changes regularly, and the output force (moment) does not appear chattering and mutation when the disturbance signal is added. The research results can provide support for the further study of picking manipulator trajectory tracking control system.
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Wang, Qian, Boyan Cai, Yajie Yu, and Hui Cao. "Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization." Journal of Spectroscopy 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/5616503.
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Spectroscopy is an efficient and widely used quantitative analysis method. In this paper, a spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. For the proposed method, backpropagation neural network is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of neural network is realized by nonlinear adaptive evolutionary programming (NAEP). The hybrid chromosome in binary scheme of NAEP has three parts. The first part represents the topology structure of neural network, the second part represents the selection of wavelengths in the spectral data, and the third part represents the parameters of mutation of NAEP. Two real flue gas datasets are used in the experiments. In order to present the effectiveness of the methods, the partial least squares with full spectrum, the partial least squares combined with genetic algorithm, the uninformative variable elimination method, the backpropagation neural network with full spectrum, the backpropagation neural network combined with genetic algorithm, and the proposed method are performed for building the component prediction model. Experimental results verify that the proposed method has the ability to predict more accurately and robustly as a practical spectral analysis tool.
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Tran, Duc Hoc, and Luong Duc Long. "Project scheduling with time, cost and risk trade-off using adaptive multiple objective differential evolution." Engineering, Construction and Architectural Management 25, no. 5 (June 18, 2018): 623–38. http://dx.doi.org/10.1108/ecam-05-2017-0085.
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PurposeAs often in project scheduling, when the project duration is shortened to reduce total cost, the total float is lost resulting in more critical or nearly critical activities. This, in turn, results in reducing the probability of completing the project on time and increases the risk of schedule delays. The objective of project management is to complete the scope of work on time, within budget in a safe fashion of risk to maximize overall project success. The purpose of this paper is to present an effective algorithm, named as adaptive multiple objective differential evolution (DE) for project scheduling with time, cost and risk trade-off (AMODE-TCR).Design/methodology/approachIn this paper, a multi-objective optimization model for project scheduling is developed using DE algorithm. The AMODE modifies a population-based search procedure by using adaptive mutation strategy to prevent the optimization process from becoming a purely random or a purely greedy search. An elite archiving scheme is adopted to store elite solutions and by aptly using members of the archive to direct further search.FindingsA numerical construction project case study demonstrates the ability of AMODE in generating non-dominated solutions to assist project managers to select an appropriate plan to optimize TCR problem, which is an operation that is typically difficult and time-consuming. Comparisons between the AMODE and currently widely used multiple objective algorithms verify the efficiency and effectiveness of the developed algorithm. The proposed model is expected to help project managers and decision makers in successfully completing the project on time and reduced risk by utilizing the available information and resources.Originality/valueThe paper presented a novel model that has three main contributions: First, this paper presents an effective and efficient adaptive multiple objective algorithms named as AMODE for producing optimized schedules considering time, cost and risk simultaneously. Second, the study introduces the effect of total float loss and resource control in order to enhance the schedule flexibility and reduce the risk of project delays. Third, the proposed model is capable of operating automatically without any human intervention.
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Zhao, Junqing, and Pengfei Tie. "Design and Implementation of Energy-Saving Logistics Management System for Route Optimization." Wireless Communications and Mobile Computing 2022 (August 30, 2022): 1–6. http://dx.doi.org/10.1155/2022/8389468.
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In order to effectively solve the problem of vehicle routing, a design and implementation method of an energy-saving logistics management system oriented to routing optimization is proposed. From the perspective of optimal calculation, this research uses the improved Dixie algorithm and clustering algorithm to design and implement a logistics company’s distribution center location and distribution path planning system. First of all, the authors analyze the common models of the LRP problem in detail and give the mathematical model and calculation method of positioning rationing and the transportation route planning problem. Secondly, in view of the shortcomings of traditional evolutionary algorithms, the authors propose a series of improvement measures. The authors adopt a natural number coding scheme combined with an adaptive crossover mutation operator to improve the search ability of the solution space; the authors also introduce a penalty function to deal with constraints and take corresponding measures for illegal individuals generated in the evolution process, reducing premature convergence. Possibility. It has been verified that the design and development of the system saves investment costs for small and medium-sized logistics enterprises and reduces the cost of goods distribution by 80%. The effect is remarkable, which verifies the effectiveness, accuracy, and superiority of the algorithm.
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Zhang, Huaying, Bin Xiao, Jinqiong Li, and Min Hou. "An Improved Genetic Algorithm and Neural Network-Based Evaluation Model of Classroom Teaching Quality in Colleges and Universities." Wireless Communications and Mobile Computing 2021 (August 17, 2021): 1–7. http://dx.doi.org/10.1155/2021/2602385.
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Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational management requires the development and refinement of a system for evaluating teaching quality. Traditional approaches to assessing teaching quality, on the other hand, are problematic due to their limitations. As a result, a scientific and reasonable model for evaluating the teaching quality of college undergraduate teachers must be developed. We present a unique model for evaluating the quality of classroom teaching in colleges and universities, which is based on improved genetic algorithms and neural networks. The basic idea is to use adaptive mutation genetic algorithms to refine the initial weights and thresholds of the BP neural network. The teaching quality evaluation findings were improved by improving the neural network’s prediction accuracy and convergence speed, resulting in a more practical scheme for evaluating college and university teaching quality. We have conducted simulation experiments and comparative analysis, and the mean square error of the results of the proposed model is very low, which proves the effectiveness and superiority of the algorithm.
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Hu, Yuan, Zhaohong Bie, Yanling Lin, Guangtao Ning, Mingfan Chen, and Yujie Gao. "Multiobjective Transmission Network Planning considering the Uncertainty and Correlation of Wind Power." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/207428.
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In order to consider the uncertainty and correlation of wind power in multiobjective transmission network expansion planning (TNEP), this paper presents an extended point-estimation method to calculate the probabilistic power flow, based on which the correlative power outputs of wind farm are sampled and the uncertain multiobjective transmission network planning model is transformed into a solvable deterministic model. A modified epsilon multiobjective evolutionary algorithm is used to solve the above model and a well-distributed Pareto front is achieved, and then the final planning scheme can be obtained from the set of nondominated solutions by a fuzzy satisfied method. The proposed method only needs the first four statistical moments and correlation coefficients of the output power of wind farms as input information; the modeling of wind power is more precise by considering the correlation between wind farms, and it can be easily combined with the multiobjective transmission network planning model. Besides, as the self-adaptive probabilities of crossover and mutation are adopted, the global search capabilities of the proposed algorithm can be significantly improved while the probability of being stuck in the local optimum is effectively reduced. The accuracy and efficiency of the proposed method are validated by IEEE 24 as well as a real system.
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Yang, Fan, Yuefeng Du, Wei Li, Zhen Li, Enrong Mao, and Zhongxiang Zhu. "Innovative Design Method of Hydro-Pneumatic Suspension for Large High-Clearance Sprayer Based on Improved NSGA-II Algorithm." Agriculture 13, no. 5 (May 17, 2023): 1071. http://dx.doi.org/10.3390/agriculture13051071.
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Large high-clearance sprayers are widely used in the field of plant protection due to their high work efficiency. Influenced by the characteristics of a large ground clearance, fast driving speed and constantly changing sprung mass, how to solve the contradiction between the vibration reduction performance of a large sprayer and the friendliness of farmland roads has become a current research hotspot. In order to improve the driving performance of the sprayers, the design, optimization and verification scheme of the hydro-pneumatic suspension of a large sprayer based on the improved NSGA-II algorithm was completely constructed in this study. The hydro-pneumatic suspension system of a sprayer was mainly designed and a real-time time-varying model under field road excitation was established. The NSGA-II algorithm was improved by introducing the adaptive crossover operator and DE mutation operator, and a real-time interactive interface between the time-varying model was established for multi-objective optimization. Finally, system simulation analysis was conducted and a vibration test bench was built for experimental verification. The results show that vibration reduction indicators improved by 19.4%, 10.7% and 4.0%, respectively, compared with those before optimization. The performance of the designed hydro-pneumatic suspension was better than that of the ordinary suspension.
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Wang, Yutang, Dapeng Tian, and Ming Dai. "Composite Hierarchical Anti-Disturbance Control with Multisensor Fusion for Compact Optoelectronic Platforms." Sensors 18, no. 10 (September 21, 2018): 3190. http://dx.doi.org/10.3390/s18103190.
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In the aerospace field, compact optoelectronic platforms (COPs) are being increasingly equipped on unmanned aircraft systems (UAS). They assist UAS in a range of mission-specific tasks such as disaster relief, crop testing, and firefighting. However, the strict constraint of structure space makes COPs subject to multi-source disturbances. The application of a low-cost and low-precision sensor also affects the system control performance. A composite hierarchical anti-disturbance control (CHADC) scheme with multisensor fusion is explored herein to improve the motion performance of COPs in the presence of internal and external disturbances. Composite disturbance modelling combining the characteristic of wire-wound moment is presented in the inner layer. The adaptive mutation differential evolution algorithm is implemented to identify and optimise the model parameters of the system internal disturbance. Inverse model compensation and finite-time nonlinear disturbance observer are then constructed to compensate for multiple disturbances. A non-singular terminal sliding mode controller is constructed to attenuate disturbance in the outer layer. A stability analysis for both the composite disturbance compensator and the closed-loop system is provided using Lyapunov stability arguments. The phase lag-free low-pass filter is implemented to interfuse multiple sensors with different order information and achieve satisfactory noise suppression without phase lag. Experimental results demonstrate that the proposed CHADC strategy with a higher-quality signal has an improved performance for multi-source disturbance compensation.
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Guo, Fumin, Hua Zhang, Yilu Xu, Genliang Xiong, and Cheng Zeng. "Isokinetic Rehabilitation Trajectory Planning of an Upper Extremity Exoskeleton Rehabilitation Robot Based on a Multistrategy Improved Whale Optimization Algorithm." Symmetry 15, no. 1 (January 13, 2023): 232. http://dx.doi.org/10.3390/sym15010232.
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Upper extremity exoskeleton rehabilitation robots have become a significant piece of rehabilitation equipment, and planning their motion trajectories is essential in patient rehabilitation. In this paper, a multistrategy improved whale optimization algorithm (MWOA) is proposed for trajectory planning of upper extremity exoskeleton rehabilitation robots with emphasis on isokinetic rehabilitation. First, a piecewise polynomial was used to construct a rough trajectory. To make the trajectory conform to human-like movement, a whale optimization algorithm (WOA) was employed to generate a bounded jerk trajectory with the minimum running time as the objective. The search performance of the WOA under complex constraints, including the search capability of trajectory planning symmetry, was improved by the following strategies: a dual-population search, including a new communication mechanism to prevent falling into the local optimum; a mutation centroid opposition-based learning, to improve the diversity of the population; and an adaptive inertia weight, to balance exploration and exploitation. Simulation analysis showed that the MWOA generated a trajectory with a shorter run-time and better symmetry and robustness than the WOA. Finally, a pilot rehabilitation session on a healthy volunteer using an upper extremity exoskeleton rehabilitation robot was completed safely and smoothly along the trajectory planned by the MWOA. The proposed algorithm thus provides a feasible scheme for isokinetic rehabilitation trajectory planning of upper extremity exoskeleton rehabilitation robots.
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Huang, Fan, Haiping Zhang, Qiaofeng Wu, Shanqing Chi, and Mingqing Yang. "An Optimal Model and Application of Hydraulic Structure Regulation to Improve Water Quality in Plain River Networks." Water 15, no. 24 (December 17, 2023): 4297. http://dx.doi.org/10.3390/w15244297.
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The proper dispatching of hydraulic structures in water diversion projects is a desirable way to maximize project benefits. This study aims to provide a reliable, optimal scheduling model for hydraulic engineering to improve the regional water environment. We proposed an improved gravitational search algorithm (IPSOGSA) based on multi-strategy hybrid technology to solve this practical problem. The opposition-based learning strategy, elite mutation strategy, local search strategy, and co-evolution strategies were employed to balance the exploration and exploitation of the algorithm through the adaptive evolution of the elite group. Compared with several other algorithms, the preponderance of the proposed algorithm in single-objective optimization problems was demonstrated. We combined the water quality mechanism model, an artificial neural network (ANN), and the proposed algorithm to establish the optimal scheduling model for hydraulic structures. The backpropagation neural network (IGSA-BPNN) trained by the improved algorithm has a high accuracy, with a coefficient of determination (R2) over 0.95. Compared to the two traditional algorithms, the IGSA-BPNN model was, respectively, improved by 1.5% and 0.9% on R2 in the train dataset, and 1.1% and 1.5% in the test dataset. The optimal scheduling model for hydraulic structures led to a reduction of 46~69% in total power consumption while achieving the water quality objectives. With the lowest cost scheme in practice, the proposed intelligent scheduling model is recommended for water diversion projects in plain river networks.
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Qu, Dan, Hualin Xiao, Huafei Chen, and Hongyi Li. "An improved differential evolution algorithm for multi-modal multi-objective optimization." PeerJ Computer Science 10 (March 14, 2024): e1839. http://dx.doi.org/10.7717/peerj-cs.1839.
Full textAbstract:
Multi-modal multi-objective problems (MMOPs) have gained much attention during the last decade. These problems have two or more global or local Pareto optimal sets (PSs), some of which map to the same Pareto front (PF). This article presents a new affinity propagation clustering (APC) method based on the Multi-modal multi-objective differential evolution (MMODE) algorithm, called MMODE_AP, for the suit of CEC’2020 benchmark functions. First, two adaptive mutation strategies are adopted to balance exploration and exploitation and improve the diversity in the evolution process. Then, the affinity propagation clustering method is adopted to define the crowding degree in decision space (DS) and objective space (OS). Meanwhile, the non-dominated sorting scheme incorporates a particular crowding distance to truncate the population during the environmental selection process, which can obtain well-distributed solutions in both DS and OS. Moreover, the local PF membership of the solution is defined, and a predefined parameter is introduced to maintain of the local PSs and solutions around the global PS. Finally, the proposed algorithm is implemented on the suit of CEC’2020 benchmark functions for comparison with some MMODE algorithms. According to the experimental study results, the proposed MMODE_AP algorithm has about 20 better performance results on benchmark functions compared to its competitors in terms of reciprocal of Pareto sets proximity (rPSP), inverted generational distances (IGD) in the decision (IGDX) and objective (IGDF). The proposed algorithm can efficiently achieve the two goals, i.e., the convergence to the true local and global Pareto fronts along with better distributed Pareto solutions on the Pareto fronts.