Journal articles: 'REAL CODED GENETIC ALGORITHM (RCGA)'

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Beklaryan, Gayane L., Andranik S. Akopov, and Nerses K. Khachatryan. "Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 87–103. http://dx.doi.org/10.2478/cait-2019-0017.

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Abstract This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models (SD-models). The main feature of the genetic algorithm presented herein is the application of fuzzy control to its parameters, such as the probability of a mutation, type of crossover operator, size of the parent population, etc. The control rules for the Real-Coded Genetic Algorithm (RCGA) were suggested based on the estimation of the values of the performance metrics, such as rate of convergence, processing time and remoteness from a potential extremum. Results of optimisation experiments demonstrate the greater time-efficiency of F-RCGA in comparison with other RCGAs, as well as the Monte-Carlo method. F-RCGA was validated by using well-known test instances and applied for the optimisation of characteristics of some system dynamics models.

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Akopov, Andranik S., Levon A. Beklaryan, and Armen L. Beklaryan. "Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation." Cybernetics and Information Technologies 21, no. 3 (September 1, 2021): 127–44. http://dx.doi.org/10.2478/cait-2021-0034.

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Abstract This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

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Mahmudy, Wayan F., Romeo M. Marian, and Lee H. S. Luong. "Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization." Advanced Materials Research 701 (May 2013): 364–69. http://dx.doi.org/10.4028/www.scientific.net/amr.701.364.

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This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple genetic operators and random initial population, the proposed RCGA can produce promising results comparable to those achieved by other best-known approaches in the literatures. These results demonstrate the robustness of the RCGA.

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Uemura, Kento, and Isao Ono. "AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 3 (May 19, 2016): 429–37. http://dx.doi.org/10.20965/jaciii.2016.p0429.

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This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adapts the width of the offspring distribution automatically to improve its search efficiency. We evaluate the performance of AEGA using benchmark problems and show that AEGA finds the optimum with fewer evaluations than mAREX/JGG with a maximum reduction ratio of 45%. Furthermore, we apply AEGA to a lens design problem that is known as a difficult real-world problem and show that AEGA reaches the known best solution with approximately 25% fewer evaluations than mAREX/JGG.

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Selvakumari Jeya, I. Jasmine, and S. N. Deepa. "Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier." Computational and Mathematical Methods in Medicine 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7493535.

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A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

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Cherif, Imen, and Farhat Fnaiech. "Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)." International Journal of Applied Mathematics and Computer Science 25, no. 4 (December 1, 2015): 863–75. http://dx.doi.org/10.1515/amcs-2015-0062.

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Abstract This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming.

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Akopov, Andranik S., Levon A. Beklaryan, and Armen L. Beklaryan. "Cluster-Based Optimization of an Evacuation Process Using a Parallel Bi-Objective Real-Coded Genetic Algorithm." Cybernetics and Information Technologies 20, no. 3 (September 1, 2020): 45–63. http://dx.doi.org/10.2478/cait-2020-0027.

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AbstractThis work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were compared with other well-known multi-objective genetic algorithms (e.g., -MOEA, NSGA-II, SPEA2). Moreover, P-RCGA was aggregated with the developed simulation of the behavior of human agent-rescuers in emergency through the objective functions to optimize the main parameters of the evacuation process.

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Nakane, Takumi, Xuequan Lu, and Chao Zhang. "A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm." Computational Intelligence and Neuroscience 2020 (September 27, 2020): 1–20. http://dx.doi.org/10.1155/2020/8835852.

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In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered, and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 15 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of both accuracy and convergence speed. Also, the induced additional runtime is negligible compared to the total processing time.

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Padmanabhan, S., M. Chandrasekaran, P. Asokan, and V. Srinivasa Raman. "A Performance Study of Real Coded Genetic Algorithm on Gear Design Optimization." Advanced Materials Research 622-623 (December 2012): 64–68. http://dx.doi.org/10.4028/www.scientific.net/amr.622-623.64.

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he major problem that deals with practical engineers is the mechanical design and creativeness. Mechanical design can be defined as the choice of materials and geometry, which satisfies, specified functional requirements of that design. A good design has to minimize the most significant adverse result and to maximize the most significant desirable result. An evolutionary algorithm offers efficient ways of creating and comparing a new design solution in order to complete an optimal design. In this paper a type of Genetic Algorithm, Real Coded Genetic Algorithm (RCGA) is used to optimize the design of helical gear pair and a combined objective function with maximizes the Power, Efficiency and minimizes the overall Weight, Centre distance. The performance of the proposed algorithms is validated through LINGO Software and the comparative results are analyzed.

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Lin, W., M. H. Wu, and S. Duan. "Engine Test Data Modelling by Evolutionary Radial Basis Function Networks." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 217, no. 6 (June 1, 2003): 489–97. http://dx.doi.org/10.1243/095440703766518113.

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The engine test bed is introduced briefly and the importance of modelling for the engine test is discussed. The application of combining radial basis function (RBF) networks and a real-coded genetic algorithm (RCGA) to create the model is described for the engine test. Finally, the experimental results are analysed and it is shown that the proposed approach combining RCGA and RBF models is well suited for the engine test data modelling task.

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Kita, Hajime. "A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms." Evolutionary Computation 9, no. 2 (June 2001): 223–41. http://dx.doi.org/10.1162/106365601750190415.

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This paper discusses the self-adaptive mechanisms of evolution strategies (ES) and real-coded genetic algorithms (RCGA) for optimization in continuous search spaces. For multi-membered evolution strategies, a self-adaptive mechanism of mutation parameters has been proposed by Schwefel. It introduces parameters such as standard deviations of the normal distribution for mutation into the genetic code and lets them evolve by selection as well as the decision variables. In the RCGA, crossover or recombination is used mainly for search. It utilizes information on several individuals to generate novel search points, and therefore, it can generate offspring adaptively according to the distribution of parents without any adaptive parameters. The present paper discusses characteristics of these two self-adaptive mechanisms through numerical experiments. The self-adaptive characteristics such as translation, enlargement, focusing, and directing of the distribution of children generated by the ES and the RCGA are examined through experiments.

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Sawyerr, Babatunde A., Aderemi O. Adewumi, and M. Montaz Ali. "Benchmarking RCGAu on the Noiseless BBOB Testbed." Scientific World Journal 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/734957.

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RCGAu is a hybrid real-coded genetic algorithm with “uniform random direction” search mechanism. Theuniform random directionsearch mechanism enhances the local search capability of RCGA. In this paper, RCGAu was tested on the BBOB-2013 noiseless testbed using restarts till a maximum number of function evaluations (#FEs) of 105×Dare reached, whereDis the dimension of the function search space. RCGAu was able to solve several test functions in the low search dimensions of 2 and 3 to the desired accuracy of 108. Although RCGAu found it difficult in getting a solution with the desired accuracy 108for high conditioning and multimodal functions within the specified maximum #FEs, it was able to solve most of the test functions with dimensions up to 40 with lower precisions.

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Mahmudy, Wayan Firdaus, Romeo M. Marian, and Lee H. S. Luong. "Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling." Advanced Materials Research 701 (May 2013): 359–63. http://dx.doi.org/10.4028/www.scientific.net/amr.701.359.

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This paper and its companion (Part 2) deal with modelling and optimization of the flexible job-shop problem (FJSP). The FJSP is a generalised form of the classical job-shop problem (JSP) which allows an operation to be processed on several alternatives machines. To solve this NP-hard combinatorial problem, this paper proposes a customised Genetic Algorithm (GA) which uses an array of real numbers as chromosome representation so the proposed GA is called a real-coded GA (RCGA). The novel chromosome representation is designed to produces only feasible solutions which can be used to effectively explore the feasible search space. This first part of the papers focuses on the modelling of the problems and discusses how the novel chromosome representation can be decoded into a feasible solution. The second part will discuss genetic operators and the effectiveness of the RCGA to solve various test bed problems from literature.

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Li, Ye, and Xiaohu Shi. "Mine Pressure Prediction Study Based on Fuzzy Cognitive Maps." International Journal of Computational Intelligence and Applications 19, no. 03 (August 5, 2020): 2050023. http://dx.doi.org/10.1142/s1469026820500236.

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The study on the prediction of mine pressure, while exploiting in coal mine, is a critical and technical guarantee for coal mine safety and production. In this paper, primarily due to the actual demand for the prediction of mine pressure, a practical prediction model Mine Pressure Prediction (MPP) was proposed based on fuzzy cognitive maps (FCMs). The Real Coded Genetic Algorithm (RCGA) was proposed to solve the problem by introducing the weight regularization and dropout regularization. A numerical example involving in-situ monitoring data is studied. Mean Square Error (MSE) and fitness function were used to evaluate the applicability of MPP model which is trained by RCGA, Regularization Genetic Algorithm (RGA) and Weight and Dropout RGA optimization algorithms. The numerical results demonstrate that the proposed Weight and Dropout RGA is better than the other two algorithms, and realizing the requirement for prediction of mine pressure in the coal mine production.

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Nishiba, Ai, Hiroharu Kawanaka, Haruhiko Takase, and Shinji Tsuruoka. "A Proposal of Genetic Operations for BSIM Parameter Extraction Using Real-Coded Genetic Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (October 20, 2011): 1131–38. http://dx.doi.org/10.20965/jaciii.2011.p1131.

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This paper discusses genetic operations and their effects on evolution of GA in BSIM parameter extraction problems. Generally, Real-Coded Genetic Algorithm (RCGA) using Simplex Crossover (SPX) is often employed to extract BSIM parameter sets. BSIM parameters, however, have recommended operating ranges. There are regarded as constraints, thus all extracted parameters have to be satisfied them. In many cases, when the number of parameters becomes large, the conventional methods generate a lot of infeasible solutions because SPX makes offspring on the simplex plane expanded by ε parameter. This makes search efficiency of GA reduce drastically. Because of these factors, we propose genetic operations considering the constraints to prevent reduction of search efficiency of GA. In this paper, some experiments using actual static characteristic curves of MOS-FET were conducted to validate the proposed method. This paper also discussed the effectiveness of the proposed method.

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Bhosale, K. C., and P. J. Pawar. "Material Flow Optimisation of Flexible Manufacturing System using Real Coded Genetic Algorithm (RCGA)." Materials Today: Proceedings 5, no. 2 (2018): 7160–67. http://dx.doi.org/10.1016/j.matpr.2017.11.381.

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Wang, Jiquan, Mingxin Zhang, Okan K. Ersoy, Kexin Sun, and Yusheng Bi. "An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover." Computational Intelligence and Neuroscience 2019 (November 14, 2019): 1–17. http://dx.doi.org/10.1155/2019/4243853.

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A multi-offspring improved real-coded genetic algorithm (MOIRCGA) using the heuristical normal distribution and direction-based crossover (HNDDBX) is proposed to solve constrained optimization problems. Firstly, a HNDDBX operator is proposed. It guarantees the cross-generated offsprings are located near the better individuals in the population. In this way, the HNDDBX operator ensures that there is a great chance of generating better offsprings. Secondly, as iterations increase, the same individuals are likely to appear in the population. Therefore, it is possible that the two parents of participation crossover are the same. Under these circumstances, the crossover operation does not generate new individuals, and therefore does not work. To avoid this problem, the substitution operation is added after the crossover so that there is no duplication of the same individuals in the population. This improves the computational efficiency of MOIRCGA by leading it to quickly converge to the global optimal solution. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, a Combinational Mutation method is proposed with both local search and global search. The experimental results with sixteen examples show that the multi-offspring improved real-coded genetic algorithm (MOIRCGA) has fast convergence speed. As an example, the optimization model of the cantilevered beam structure is formulated, and the proposed MOIRCGA is compared to the RCGA in optimizing the parameters of the cantilevered beam structure. The optimization results show that the function value obtained with the proposed MOIRCGA is superior to that of RCGA.

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Zhang, Pei, and Jian Feng. "Critical Buckling of Prestress-Stable Tensegrity Structures Solved by Real-Coded Genetic Algorithm." International Journal of Structural Stability and Dynamics 18, no. 04 (March 28, 2018): 1850048. http://dx.doi.org/10.1142/s0219455418500487.

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Tensegrity structures are classified as kinematically determinate ones with two subcases and kinematically indeterminate ones with three subcases in view of their respective stability properties. How the stiffness of a tensegrity structure changes as the level of prestress changes is explored for different scenarios using six carefully chosen samples. For a tensegrity structure merely satisfying the prestress-stability condition, a new optimization model is presented to determine its critical buckling state corresponding to zero stiffness. A real-coded genetic algorithm (RCGA) is then developed to solve this problem, featured by the fact that a special sign-control technique is embedded in the fundamental genetic operations, making the individuals generated in each step fall into the admissible region automatically. The stability of the neutral equilibrium state for tensegrity structures violating the prestress-stability condition is also discussed. Several numerical examples are tested to validate the efficiency of the present approach.

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Kubba, Hassan Abdullah, and Alaa Suheib Rodhan. "A Real-Coded Genetic Algorithm with System Reduction and Restoration for Rapid and Reliable Power Flow Solution of Power Systems." Journal of Engineering 21, no. 5 (May 1, 2015): 1–19. http://dx.doi.org/10.31026/j.eng.2015.05.01.

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The paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms of the calculated voltages of the generator buses, after a derivation of equations for calculating the voltages of the load busbars. The proposed method was demonstrated on 14-bus IEEE test systems and the practical system 362-busbar IRAQI NATIONAL GRID (ING). The proposed method has reliable convergence, a highly accurate solution and less computing time for on-line applications. The method can conveniently be applied for on-line analysis and planning studies of large power systems.

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Akopov, Andranik. "Modeling and optimization of strategies for making individual decisions in multi-agent socio-economic systems with the use of machine learning." Business Informatics 17, no. 2 (June 30, 2023): 7–19. http://dx.doi.org/10.17323/2587-814x.2023.2.7.19.

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This article presents a new approach to modeling and optimizing individual decision-making strategies in multi-agent socio-economic systems (MSES). This approach is based on the synthesis of agent-based modeling methods, machine learning and genetic optimization algorithms. A procedure for the synthesis and training of artificial neural networks (ANNs) that simulate the functionality of MSES and provide an approximation of the values of its objective characteristics has been developed. The feature of the two-step procedure is the combined use of particle swarm optimization methods (to determine the optimal values of hyperparameters) and the Adam machine learning algorithm (to compute weight coefficients of the ANN). The use of such ANN-based surrogate models in parallel multi-agent real-coded genetic algorithms (MA-RCGA) makes it possible to raise substantially the time-efficiency of the evolutionary search for optimal solutions. We have conducted numerical experiments that confirm a significant improvement in the performance of MA-RCGA, which periodically uses the ANN-based surrogate-model to approximate the values of the objective and fitness functions. A software framework has been designed that consists of the original (reference) agent-based model of trade interactions, the ANN-based surrogate model and the MA-RCGA genetic algorithm. At the same time, the software libraries FLAME GPU, OpenNN (Open Neural Networks Library), etc., agent-based modeling and machine learning methods are used. The system we developed can be used by responsible managers.

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Akopov, Andranik S., Armen L. Beklaryan, and Aleksandra A. Zhukova. "Optimization of Characteristics for a Stochastic Agent-Based Model of Goods Exchange with the Use of Parallel Hybrid Genetic Algorithm." Cybernetics and Information Technologies 23, no. 2 (June 1, 2023): 87–104. http://dx.doi.org/10.2478/cait-2023-0015.

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Abstract A novel approach to modeling stochastic processes of goods exchange between multiple agents is presented, considering the possibility of optimizing the environment's characteristics and individual decision-making strategies. The proposed model makes it possible to form optimal states when choosing the moments of concluding barter and monetary transactions at the individual level of each agent maximizing the utility function. A new parallel hybrid Real-Coded Genetic Algorithm and Particle Swarm Optimization (RCGA-PSO) has been developed, combining methods of evolutionary selection based on well-known heuristic operators with methods of swarm optimization and machine learning. The algorithm is characterized by the best time efficiency and accuracy in comparison with other methods. The software implementation of the developed algorithm and model has been performed using the FLAME GPU framework. The possibility of using the RCGA-PSO Algorithm to optimize the characteristics of the environment and strategies for making individual decisions by agents involved in barter and monetary interactions is demonstrated.

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Noureddine, Aloui, Mohamed Boussif, and Cherif Adnane. "A Modified Ultraspherical Window and Its Application for Speech Enhancement." Traitement du Signal 39, no. 1 (February 28, 2022): 79–86. http://dx.doi.org/10.18280/ts.390108.

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In this paper an improved ultraspherical window is developed for designing quadrature mirror filters banks (QMF) with the help of a real coded genetic algorithm (RCGA). In fact, the ultraspherical window is modified by adding a parameter (α) which to improve the spectral parameters. Then, RCGA is used to find optimal values of the adjustment parameters, the side-lobes ratio of ultraspherical window and the cut-off frequency of the low-pass prototype filter. This latter, is used to derive all the filters of QMF banks. When the developed QMF banks are exploited for speech enhancement algorithm based on wavelets techniques it gives good performances in terms of Perceptual Evaluation of Speech Quality (PESQ) and Signal to Noise Ratio (SNR). In addition to this, when compared to Dolph-Chebyshev, Kaiser and the original Ultraspherical windows the modified Ultraspherical window gives better performance referred to the obtained simulation results.

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Sachan, Ruchi, Zahid Muhammad, Jaehoon (Paul) Jeong, Chang Wook Ahn, and Hee Yong Youn. "MABC: Power-Based Location Planning with a Modified ABC Algorithm for 5G Networks." Discrete Dynamics in Nature and Society 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/4353612.

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The modernization of smart devices has emerged in exponential growth in data traffic for a high-capacity wireless network. 5G networks must be capable of handling the excessive stress associated with resource allocation methods for its successful deployment. We also need to take care of the problem of causing energy consumption during the dense deployment process. The dense deployment results in severe power consumption because of fulfilling the demands of the increasing traffic load accommodated by base stations. This paper proposes an improved Artificial Bee Colony (ABC) algorithm which uses the set of variables such as the transmission power and location of each base station (BS) to improve the accuracy of localization of a user equipment (UE) for the efficient energy consumption at BSes. To estimate the optimal configuration of BSes and reduce the power requirement of connected UEs, we enhanced the ABC algorithm, which is named a Modified ABC (MABC) algorithm, and compared it with the latest work on Real-Coded Genetic Algorithm (RCGA) and Differential Evolution (DE) algorithm. The proposed algorithm not only determines the optimal coverage of underutilized BSes but also optimizes the power utilization considering the green networks. The performance comparisons of the modified algorithms were conducted to show that the proposed approach has better effectiveness than the legacy algorithms, ABC, RCGA, and DE.

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Ly, Hai-Bang, Tien-Thinh Le, Huong-Lan Thi Vu, Van Quan Tran, Lu Minh Le, and Binh Thai Pham. "Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams." Sustainability 12, no. 7 (March 30, 2020): 2709. http://dx.doi.org/10.3390/su12072709.

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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Rao, R. V., and R. B. Pawar. "Quasi-oppositional-based Rao algorithms for multi-objective design optimization of selected heat sinks." Journal of Computational Design and Engineering 7, no. 6 (August 12, 2020): 830–63. http://dx.doi.org/10.1093/jcde/qwaa060.

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Abstract In this paper, an endeavor is made to enhance the convergence speed of the recently proposed Rao algorithms. The new upgraded versions of Rao algorithms named as “quasi-oppositional-based Rao algorithms” are proposed in this paper. The quasi-oppositional-based learning is incorporated in the basic Rao algorithms to diversify the searching process of the algorithms. The performance of the proposed algorithms is tested on 51 unconstrained benchmark functions. Also, three multi-objective optimization case studies of different heat sinks such as a single-layered microchannel heat sink (SL-MCHS), a double-layered microchannel heat sink (DL-MCHS), and a plate-fin heat sink (PFHS) are attempted to investigate the effectiveness of the proposed algorithms in solving real-world complex engineering optimization problems. The results obtained using the proposed algorithms are compared with the results obtained using the well-known advanced optimization algorithms such as genetic algorithm (GA), artificial bee colony (ABC), differential evolution (DE), particle swarm optimization (PSO), teaching-learning-based algorithm (TLBO), Jaya algorithm, multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA-II), real-coded GA (RCGA), direction-based GA, self-adaptive multi-population (SAMP) Rao algorithms, and basic Rao algorithms. The proposed quasi-oppositional-based Rao algorithms are found superior or competitive to the other optimization algorithms considered.

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Y, Ainul, H. M., Salleh, S. M, Halib, N, Taib, H., and Fathi, M. S. "Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 443. http://dx.doi.org/10.14419/ijet.v7i4.30.22363.

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System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

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Le, Tien-Thinh. "Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading." Advances in Civil Engineering 2020 (October 28, 2020): 1–19. http://dx.doi.org/10.1155/2020/8832522.

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In this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. The proposed model was Adaptive Neurofuzzy Inference System (ANFIS) combined with Real Coded Genetic Algorithm (RCGA), denoted as RCGA-ANFIS. The evaluation of the model was performed using the coefficient of determination (R2) and root mean square error (RMSE). The results showed that the RCGA-ANFIS (R2 = 0.974) was more reliable and effective than conventional gradient descent (GD) technique (R2 = 0.952). The accuracy of the present work was found superior to the results published in the literature (R2 = 0.776 or 0.768) when predicting the load capacity of elliptical CFST columns. Finally, sensitivity analysis showed that the thickness of the steel tube and the minor axis length of the elliptical cross section were the most influential parameters. For practical application, a Graphical User Interface (GUI) was developed in MATLAB for researchers and engineers and to support the teaching and interpretation of the axial behavior of CFST columns.

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Ameerudden, Mohammad Riyad, and Harry C. S. Rughooputh. "Hybridized Genetic Algorithms in the Optimization of a PIFA Antenna Using Fitness Characterization and Clustering." Advanced Materials Research 622-623 (December 2012): 40–44. http://dx.doi.org/10.4028/www.scientific.net/amr.622-623.40.

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With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, new antennas should be developed to provide both larger bandwidth and small dimensions. The aim of this project is to design and optimize the bandwidth of a Planar Inverted-F Antenna (PIFA) in order to achieve a larger bandwidth in the 2 GHz band. This paper presents an intelligent optimization technique using a hybridized Genetic Algorithms (GA) coupled with the intelligence of the Binary String Fitness Characterization (BSFC) technique. The optimization technique used is based on the Binary Coded GA (BCGA) and Real-Coded GA (RCGA). The process has been further enhanced by using a Clustering Algorithm to minimize the computational cost. Using the Hybridized GA with BSFC and Clustering, the bandwidth evaluation process has been observed to be more efficient combining both high performance and minimal computational cost. During the optimization process, the different PIFA models are evaluated using the finite-difference time domain (FDTD) method.

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So, GunBaek. "Design of an Intelligent NPID Controller Based on Genetic Algorithm for Disturbance Rejection in Single Integrating Process with Time Delay." Journal of Marine Science and Engineering 9, no. 1 (December 29, 2020): 25. http://dx.doi.org/10.3390/jmse9010025.

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The integrating process with time delay (IPTD) is a fundamentally unstable open-loop system due to poles at the origin of the transfer function, and designing controllers with satisfactory control performance is very difficult because of the associated time delay, which is a nonlinear element. Therefore, this study focuses on the design of an intelligent proportional-integral-derivative (PID) controller to improve the regulatory response performance to disturbance in an IPTD, and addresses problems related to optimally tuning each parameter of the controller with a real coded genetic algorithm (RCGA). Each gain of the nonlinear PID (NPID) controller consists of a product of the gains of the linear PID controller and a simple nonlinear function. Each of these nonlinear functions changes the gains in the controller to on line by nonlinearly scaling the error signal. A lead-lag compensator or first-order filter is also added to the controller to mitigate noise, which is a disadvantage of ideal derivative action. The parameters in the controller are optimally tuned by minimizing the integral of time-weighted absolute error (ITAE) using a RCGA. The proposed method is compared with three other methods through simulation to verify its effectiveness.

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Kubba, Hassan Abdullah, and Mounir Thamer Esmieel. "Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems." Journal of Engineering 24, no. 3 (March 1, 2018): 84. http://dx.doi.org/10.31026/j.eng.2018.03.07.

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Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.

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Ahila, M. Jeraldin, S. Joseph Jawhar, and N. Albert Singh. "Generation Unit Capacity Expansion Planning Analysis: Approach Using Real Coded Improved Genetic Algorithm." Applied Mechanics and Materials 626 (August 2014): 190–96. http://dx.doi.org/10.4028/www.scientific.net/amm.626.190.

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This paper provides information about the development of an algorithm called Real Coded Improved Genetic Algorithm (RCIGA). And it leads to a plan for generating units of power with minimum cost and that plan is called as Generation Unit Expansion Planning (GUEP) problem. GUEP is a fully forced non linear system. And this can be solved by technique called genetic algorithm. RCIGA helps in providing faster speed and the space which helps in searching also is increased. RCIGA helps in calculating the combination of units through which the minimum cost can be obtained and units of power should meet out the conditions of the forecasted demands.

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Liu, An, Erwie Zahara, and Ming-Ta Yang. "A Modified NM-PSO Method for Parameter Estimation Problems of Models." Journal of Applied Mathematics 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/530139.

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Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

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Mondal, Milan K., Nirmalendu Biswas, Aparesh Datta, Bikash K. Sarkar, and Nirmal K. Manna. "Positional impacts of partial wall translations on hybrid nanofluid flow in porous media: Real Coded Genetic Algorithm (RCGA)." International Journal of Mechanical Sciences 217 (March 2022): 107030. http://dx.doi.org/10.1016/j.ijmecsci.2021.107030.

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LU, WEI, LIYONG ZHANG, JIANHUA YANG, and XIAODONG LIU. "THE LINGUISTIC FORECASTING OF TIME SERIES USING IMPROVED FUZZY COGNITIVE MAP." International Journal of Computational Intelligence and Applications 12, no. 03 (September 2013): 1350014. http://dx.doi.org/10.1142/s1469026813500144.

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Most researchers of time series forecasting devote to design and develop quantitative models for pursuing high accuracy of forecasting on the numerical level. However, in real world, the numerical accuracy is sometimes not necessary for human cognition and decision-making and the numerical results of forecasting based on quantitative model are deficient in interpretability, thus the development of qualitative forecasting model of time series becomes an evident challenge. In this paper, the improved fuzzy cognitive map (IFCM) are proposed first, and then it is applied to develop qualitative model for linguistic forecasting of time series together with fuzzy c-means clustering technology and real-coded genetic algorithm (RCGA). Two real life time series are used to test the developed forecasting model and compare with another method based on FCM, whose results show the developed FCM forecasting model is more simpler and high quality on the linguistic level.

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Sudhagar, S., and V. Srinivasa Raman. "Design Optimization of Spur and Helical Gear Pairs." Applied Mechanics and Materials 766-767 (June 2015): 1034–43. http://dx.doi.org/10.4028/www.scientific.net/amm.766-767.1034.

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Gears are the most common of machine elements and due to that many studies have been conducted on optimum gear design. Gear optimization can be divided into two categories, namely, single gear pair or Gear train optimization. The problem of gear pairs design optimization is difficult to solve because it involves multiple objectives and large number of variables. Hence a trustworthy and resilient optimization technique will be more useful in obtaining an optimal solution for the problems. In the proposed work an effort has been made to optimize spur and helical gear pair design using LINGO and Meta heuristics algorithms like Real Coded Genetic Algorithm (RCGA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).On applying the combined objective function factors like Power, Efficiency is maximized and the overall Weight, Centre distance has been minimized in the model. The performance of the proposed algorithms is validated through test problems and the comparative results are reported.

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Arumugam, M. Senthil, and M. V. C. Rao. "On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems." Discrete Dynamics in Nature and Society 2006 (2006): 1–17. http://dx.doi.org/10.1155/ddns/2006/79295.

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This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm (RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

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Bhosale, K. C., and P. J. Pawar. "Material flow optimisation of production planning and scheduling problem in flexible manufacturing system by real coded genetic algorithm (RCGA)." Flexible Services and Manufacturing Journal 31, no. 2 (March 17, 2018): 381–423. http://dx.doi.org/10.1007/s10696-018-9310-5.

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Mohammed, Ali H., and Suad I. Shahl. "Impact of Distributed Generation on a Distribution Network Voltage Sags in Baghdad City." Engineering and Technology Journal 39, no. 4A (April 25, 2021): 528–42. http://dx.doi.org/10.30684/etj.v39i4a.1828.

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Voltage sags are considered as one of the most detrimental power quality (PQ) disturbance due to their costly influence on sensitive loads. This paper investigates the voltage sag mitigation in distribution network following the occurrence of a fault. Two software are used in this work; the 1st is MATLAB R2017a for implementation of the Differential Evaluation (DE) algorithm to find the optimal location and size DG and while the 2nd software is CYME 7.1 for the distribution system modelling and analysis. The effectiveness of the proposed method is tested by implementing it on IEEE 33-bus system, and then it is applied to Al-Masbh distribution network in Baghdad city as a case study. The paper aims to enhance voltage profile, power loss reduction, and relieve distribution lines overloading, by optimal placement of distributed generation (DG). The results indicate the efficiency of the proposed method comparing with Real Coded Genetic Algorithm (RCGA).

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Lee, Won Jin, and Eui Hoon Lee. "Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization." Water 14, no. 1 (January 4, 2022): 99. http://dx.doi.org/10.3390/w14010099.

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Runoff in urban streams is the most important factor influencing urban inundation. It also affects inundation in other areas as various urban streams and rivers are connected. Current runoff predictions obtained using a multi-layer perceptron (MLP) exhibit limited accuracy. In this study, the runoff of urban streams was predicted by applying an MLP using a harmony search (MLPHS) to overcome the shortcomings of MLPs using existing optimizers and compared with the observed runoff and the runoff predicted by an MLP using a real-coded genetic algorithm (RCGA). Furthermore, the results of the MLPHS were compared with the results of the MLP with existing optimizers such as the stochastic gradient descent, adaptive gradient, and root mean squared propagation. The runoff of urban steams was predicted based on the discharge of each pump station and rainfall information. The results obtained with the MLPHS exhibited the smallest error of 39.804 m3/s when compared to the peak value of the observed runoff. The MLPHS gave more accurate runoff prediction results than the MLP using the RCGA and that using existing optimizers. The accurate prediction of the runoff in an urban stream using an MLPHS based on the discharge of each pump station is possible.

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Rafique, Muhammad, Zunaib Haider, Khawaja Mehmood, Muhammad Saeed Uz Zaman, Muhammad Irfan, Saad Khan, and Chul-Hwan Kim. "Optimal Scheduling of Hybrid Energy Resources for a Smart Home." Energies 11, no. 11 (November 18, 2018): 3201. http://dx.doi.org/10.3390/en11113201.

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The present environmental and economic conditions call for the increased use of hybrid energy resources and, concurrently, recent developments in combined heat and power (CHP) systems enable their use at a domestic level. In this work, the optimal scheduling of electric and gas energy resources is achieved for a smart home (SH) which is equipped with a fuel cell-based micro-CHP system. The SH energy system has thermal and electrical loops that contain an auxiliary boiler, a battery energy storage system, and an electrical vehicle besides other typical loads. The optimal operational cost of the SH is achieved using the real coded genetic algorithm (RCGA) under various scenarios of utility tariff and availability of hybrid energy resources. The results compare different scenarios and point-out the conditions for economic operation of micro-CHP and hybrid energy systems for an SH.

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Guewouo, Thomas, Lingai Luo, Dominique Tarlet, and Mohand Tazerout. "Identification of Optimal Parameters for a Small-Scale Compressed-Air Energy Storage System Using Real Coded Genetic Algorithm." Energies 12, no. 3 (January 24, 2019): 377. http://dx.doi.org/10.3390/en12030377.

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Compressed-Air energy storage (CAES) is a well-established technology for storing the excess of electricity produced by and available on the power grid during off-peak hours. A drawback of the existing technique relates to the need to burn some fuel in the discharge phase. Sometimes, the design parameters used for the simulation of the new technique are randomly chosen, making their actual construction difficult or impossible. That is why, in this paper, a small-scale CAES without fossil fuel is proposed, analyzed, and optimized to identify the set of its optimal design parameters maximizing its performances. The performance of the system is investigated by global exergy efficiency obtained from energy and exergy analyses methods and used as an objective function for the optimization process. A modified Real Coded Genetic Algorithm (RCGA) is used to maximize the global exergy efficiency depending on thirteen design parameters. The results of the optimization indicate that corresponding to the optimum operating point, the consumed compressor electric energy is 103 . 83 k W h and the electric energy output is 25 . 82 k W h for the system charging and discharging times of about 8.7 and 2 h, respectively. To this same optimum operating point, a global exergy efficiency of 24.87% is achieved. Moreover, if the heat removed during the compression phase is accounted for in system efficiency evaluation based on the First Law of Thermodynamics, an optimal round-trip efficiency of 79.07% can be achieved. By systematically analyzing the variation of all design parameters during evolution in the optimization process, we conclude that the pneumatic motor mass flow rate can be set as constant and equal to its smallest possible value. Finally, a sensitivity analysis performed with the remaining parameters for the change in the global exergy efficiency shows the impact of each of these parameters.

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Dasgupta, Koustav, and Provas Kumar Roy. "Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm." International Journal of Applied Metaheuristic Computing 11, no. 4 (October 2020): 38–63. http://dx.doi.org/10.4018/ijamc.2020100103.

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In this article, a new optimization technique, the backtracking search algorithm (BSA), is proposed to solve the hydrothermal scheduling problem. The BSA has mainly unique five steps: (i) Initialization; (ii) Selection – I; (iii) Mutation; (iv) Crossover; and (v) Selection – II; which have been applied to minimize fuel cost of the hydro-thermal scheduling problem. The BSA is very fast, robust, reliable optimization technique and gives an accurate, optimized result. Mutation and crossover are very effective steps of the BSA, which help to determine the better optimum value of the objective function. Here, four hydro and three thermal power generating units are considered. Performance of each committed generating units (hydro and thermal) are also analyzed using a new proposed algorithm, the BSA. A multi-reservoir cascaded hydroelectric with a nonlinear relationship between water discharge rate and power generation is considered. The valve point loading effect is also considered with a fuel cost function. The proposed optimum fuel cost obtained from the BSA shows the better result as compared to other techniques like particle swarm optimization (PSO), teaching learning-based optimization (TLBO), quasi-oppositional teaching learning-based optimization (QOTLBO), real-coded genetic algorithm (RCGA), mixed-integer linear programming (MILP) and krill herd algorithm (KHA), etc.

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Papageorgiou, Poczeta, Papageorgiou, Gerogiannis, and Stamoulis. "Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece." Algorithms 12, no. 11 (November 6, 2019): 235. http://dx.doi.org/10.3390/a12110235.

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This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.

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Rai, Paridhi, and Asim Gopal Barman. "Optimizing the design of straight bevel gear with reduced scoring effect." Engineering Computations 37, no. 7 (March 11, 2020): 2391–409. http://dx.doi.org/10.1108/ec-06-2019-0250.

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Purpose The purpose of this paper is to minimize the volume of straight bevel gear and to develop resistance towards scoring failure in the straight bevel gear. Two evolutionary and more advance optimization techniques were used for performing optimization of straight bevel gears, which will also save computational time and will be less computationally expensive compared to a previously used optimization for design optimization of straight bevel gear. Design/methodology/approach The following two different cases are considered for the study: the first mathematical model similar to that used earlier and without any modification to show efficiency of the optimization algorithm for straight bevel gear design optimization and the second mathematical model consist of constraints on scoring and contact ratio along with other generally used design constraints. Real coded genetic algorithm (RCGA) and accelerated particle swarm optimization (APSO) are used to optimize the straight bevel gear design. The effectiveness of the algorithms used has been validated by comparing the obtained results with previously published results. Findings It has been found that APSO and RCGA outperform other algorithms for straight bevel gear design. Optimized design values have reduced the scoring effect significantly. The values of the contact ratio obtained further enhances the meshing operation of the bevel gear drive by making it smoother and quieter. Originality/value Low volume is one of the essential requirements of gearing applications. Scoring is a critical gear failure aspect that leads to the broken tooth in both high speed and low-speed applications of gears. The occurrence of scoring is hard to detect early and analyse. Scoring failure and contact ratio have been introduced as design constraints in the mathematical model. So, the mathematical model demonstrated in this paper minimizes the volume of the straight bevel gear drive, which has been very less attempted in previous studies, with scoring and contact ratio as some of the important design constraints, which the objective function has been subjected to. Also, two advanced and evolutionary optimization algorithms have been used to implement the mathematical model to reduce the computational time required to attain the optimal solution.

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Maiti, A. K., A. K. Bhunia, and M. Maiti. "An application of real-coded genetic algorithm (RCGA) for mixed integer non-linear programming in two-storage multi-item inventory model with discount policy." Applied Mathematics and Computation 183, no. 2 (December 2006): 903–15. http://dx.doi.org/10.1016/j.amc.2006.05.141.

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Beklaryan, Armen, Levon Beklaryan, and Andranik Akopov. "Simulation model of an intelligent transportation system for the “smart city” with adaptive control of traffic lights based on fuzzy clustering." Business Informatics 17, no. 3 (September 30, 2023): 70–86. http://dx.doi.org/10.17323/2587-814x.2023.3.70.86.

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This article presents a new simulation model of an intelligent transportation system (ITS) for the “smart city” with adaptive traffic light control. The proposed transportation model, implemented in the AnyLogic, allows us to study the behavior of interacting agents: vehicles (V) and pedestrians (P) within the framework of a multi-agent ITS of the “Manhattan Lattice” type. The spatial dynamics of agents in such an ITS is described using the systems of finite-difference equations with the variable structure, considering the controlling impact of the “smart traffic lights.” Various methods of traffic light control aimed at maximizing the total traffic of the ITS output flow have been studied, in particular, by forming the required duration phases with the use of a genetic optimization algorithm, with a local (“weakly adaptive”) switching control and based on the proposed fuzzy clustering algorithm. The possibilities of optimizing the characteristics of systems for individual control of the behavior of traffic lights under various scenarios, in particular, for the ITS with spatially homogeneous and periodic characteristics, are investigated. To determine the best values of individual parameters of traffic light control systems, such as the phases’ durations, the radius of observation of traffic and pedestrian flows, threshold coefficients, the number of clusters, etc., the previously proposed parallel real-coded genetic optimization algorithm (RCGA type) is used. The proposed method of adaptive control of traffic lights based on fuzzy clustering demonstrates greater efficiency in comparison with the known methods of collective impact and local (“weakly adaptive”) control. The results of the work can be considered a component of the decision-making system in the management of urban services.

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Kashihara, Koji. "An Intelligent Computer Assistance System for Artifact Restoration Based on Genetic Algorithms with Plane Image Features." International Journal of Computational Intelligence and Applications 16, no. 03 (September 2017): 1750021. http://dx.doi.org/10.1142/s1469026817500213.

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Restoration work of archaeological artifacts broken into pieces is similar to putting together a jigsaw puzzle. The purpose of this study is to construct an intelligent computer assistance system to conveniently restore archaeological discoveries from some fragments. AReal-Coded Genetic Algorithm (RCGA) was applicable for solving the positioning problem of a three-dimensional (3D) restoration. The fitness function value for RCGA was calculated from image similarity between the target and correct patterns in plane images at multiple camera angles. Image features of a 3D object were obtained by the ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and Accelerated KAZE (AKAZE) techniques; they were considered as a part of the fitness function value. Simulation study revealed that the RCGA approach was capable of automatically and efficiently adjusting the positions of 3D fragments, especially in the AKAZE technique. A user interface with the functions of design drawing was also created to assist in repair work. The interactive assistance interface for 3D restoration based on RCGA and followed by the hill-climbing algorithm would be applied to practical applications for digital archives of artifacts.

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Al Farizi, Muhammad Farraz, Romie Oktovianus Bura, Soleh Fajar Junjunan, and Bagus H. Jihad. "Grain Propellant Optimization Using Real Code Genetic Algorithm (RCGA)." Journal of Physics: Conference Series 1005 (April 2018): 012033. http://dx.doi.org/10.1088/1742-6596/1005/1/012033.

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Lee, Loo Hay, and Yingli Fan. "An adaptive real-coded genetic algorithm." Applied Artificial Intelligence 16, no. 6 (July 2002): 457–86. http://dx.doi.org/10.1080/08839510290030318.

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Ahn, Jong-Kap, Yun-Hyung Lee, Gang-Gyoo Jin, and Myung-Ok So. "System Identification by Real-Coded Genetic Algorithm." Journal of the Korean Society of Marine Engineering 31, no. 5 (July 31, 2007): 599–605. http://dx.doi.org/10.5916/jkosme.2007.31.5.599.

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Journal articles on the topic 'REAL CODED GENETIC ALGORITHM (RCGA)'

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