Bibliographies thématiques / Adaptive mutation scheme

Littérature scientifique sur le sujet « Adaptive mutation scheme »

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Publié le 16 juillet 2024

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Articles de revues sur le sujet "Adaptive mutation scheme"

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Bajer, Dražen. « Adaptive k-tournament mutation scheme for differential evolution ». Applied Soft Computing 85 (décembre 2019) : 105776. http://dx.doi.org/10.1016/j.asoc.2019.105776.

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Xiang, Fei, et Shan Li. « Parameter Optimization of PID Controller for Boiler Combustion System by Applying Adaptive Immune Genetic Algorithm ». Advanced Materials Research 546-547 (juillet 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 et Eugene Semenkin. « Dual-Population Adaptive Differential Evolution Algorithm L-NTADE ». Mathematics 10, no 24 (9 décembre 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 et Shui Yu. « Context-Aware Adaptive Route Mutation Scheme : A Reinforcement Learning Approach ». IEEE Internet of Things Journal 8, no 17 (1 septembre 2021) : 13528–41. http://dx.doi.org/10.1109/jiot.2021.3065680.

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Yang, Wen Xue, Zhe Chen et Cheng Jun Li. « Adaptive Clone Selection Algorithm for Function Optimization ». Applied Mechanics and Materials 644-650 (septembre 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 et JiangLiang Liu. « Research on Relay Network Method of Aerial Platform ». Journal of Physics : Conference Series 2480, no 1 (1 avril 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, et 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 (1 juillet 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 et Tao Jiang. « Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm ». Electronics 9, no 4 (23 avril 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 et Xianting Ding. « Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization ». SLAS TECHNOLOGY : Translating Life Sciences Innovation 22, no 3 (31 janvier 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 et Linjian Shangguan. « Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks ». Applied Sciences 13, no 16 (15 août 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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Chapitres de livres sur le sujet "Adaptive mutation scheme"

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Ross, Peter, et Emma Hart. « An adaptive mutation scheme for a penalty-based graph-colouring GA ». Dans Lecture Notes in Computer Science, 795–802. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0056921.

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Cheung, Bernard K. S. « Genetic Algorithm and Other Meta-Heuristics ». Dans Successful Strategies in Supply Chain Management, 144–73. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-303-6.ch007.

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Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.
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Actes de conférences sur le sujet "Adaptive mutation scheme"

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Ye, Fengming, Shingo Mabu et Kotaro Hirasawa. « A memory scheme for genetic network programming with adaptive mutation ». Dans the 13th annual conference companion. New York, New York, USA : ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2001958.

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Anik, Md Tanvir Alam, Saif Ahmed et K. M. Rakibul Islam. « Self-adaptive mutation strategy for evolutionary programming based on fitness tracking scheme ». Dans 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557833.

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Febrianti, Werry, Kuntjoro Adji Sidarto et Novriana Sumarti. « Solving some ordinary differential equations numerically using differential evolution algorithm with a simple adaptive mutation scheme ». Dans INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042351.

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Al-Ani, Dhafar, et Saeid Habibi. « A New Particle Swarm Optimization and Differential Evolution Technique for Constrained Optimization Problems ». Dans ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-63877.

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Real-world problems are often complex and may need to deal with constrained optimization problems (COPs). This has led to a growing interest in optimization techniques that involve more than one objective function to be simultaneously optimized. Accordingly, at the end of the multi-objective optimization process, there will be more than one solution to be considered. This enables a trade-off of high-quality solutions and provides options to the decision-maker to choose a solution based on qualitative preferences. Particle Swarm Optimization (PSO) algorithms are increasingly being used to solve NP-hard and constrained optimization problems that involve multi-objective mathematical representations by finding accurate and robust solutions. PSOs are currently used in many real-world applications, including (but not limited to) medical diagnosis, image processing, speech recognition, chemical reactor, weather forecasting, system identification, reactive power control, stock exchange market, and economic power generation. In this paper, a new version of Multi-objective PSO and Differential Evolution (MOPSO-DE) is proposed to solve constrained optimization problems (COPs). As presented in this paper, the proposed MOPSO-DE scheme incorporates a new leader(s) updating mechanism that is invoked when the system is under the risk of converging to premature solutions, parallel islands mechanism, adaptive mutation, and then integrated to the DE in order to update the particles’ best position in the search-space. A series of experiments are conducted using 12 well-known benchmark test problems collected from the 2006 IEEE Congress on Evolutionary Computation (CEC2006) to verify the feasibility, performance, and effectiveness of the proposed MOPSO-DE algorithm. The simulation results show the proposed MOPSO-DE is highly competitive and is able to obtain the optimal solutions for the all test problems.
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Flynn, Eric, et Michael Todd. « Optimal Sensor Placement for Active Sensing ». Dans ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2008. http://dx.doi.org/10.1115/smasis2008-439.

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We present a novel approach for optimal actuator and sensor placement for active sensing-based structural health monitoring (SHM). Of particular interest is the optimization of actuator-sensor arrays making use of Lamb wave propagation for detecting damage in thin plate-like structures. Using a detection theory framework, we establish the optimum configuration as the minimization of the expected percentage of the structure to show type I or type II error during the damage detection process. The detector incorporates a statistical model of the active sensing process which implements both pulse-echo and pitch-catch actuation schemes and takes into account line of site and non-uniform damage probabilities. The optimization space was searched using a genetic algorithm with a time varying mutation rate. We provide four example actuator/sensor placement scenarios and the optimal solutions as generated by the algorithm.
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Jain, Chandini, et Anupam Saxena. « On an Adaptive Multi-Mask Overlay Strategy for Topology Optimization of Structures and Compliant Mechanisms ». Dans ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86712.

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The honeycomb based discretization shows promise in yielding checkerboard and point flexure free optimal solutions to various topology design problems. The mesh-free material mask overlay method further promises unadulterated “black and white” optimal solutions as opposed to schemes where material is interpolated between the “void” and “filled” states in a cell [26]. Here, we propose improvements in the material mask overlay method by judiciously choosing the number of masks during a sequence of sub-searches for the optimal solution. We use an alternative mutation based zero order search which allows the use of a small population of solutions and also maintains diversity between them. Thus, multiple solutions can be simultaneously obtained for non-convex topology optimization formulations. We solve two classical problems each on optimal stiff structures and compliant mechanisms to illustrate pathology free, “black and white” topology synthesis.
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Rafael, Brigitte, Michael Affenzeller et Stefan Wagner. « An adaption of the schema theorem to various crossover and mutation operators for a music segmentation problem ». Dans the fourteenth international conference. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330856.

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