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integrating exchange market, queen bee, and shuffled complex evolution algorithms for multi-variable function optimization
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نویسنده
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salim mina ,hamedifar sima ,lotfi ali asghar
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منبع
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مهندسي برق دانشگاه تبريز - 2025 - دوره : 55 - شماره : 1 - صفحه:177 -188
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چکیده
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In this paper, three popular algorithms, including the exchange market algorithm (ema), the shuffled complex evolution (sce) algorithm, and the queen bee (qb) algorithm, are considered to propose three new hybrid evolutionary algorithms named ema-qb, ema-sce, and ema-sce-qb. then, to analyze and validate the effectiveness and efficiency of these new algorithms, we compared their performance with the performance of ema, sce, and qb algorithms on 12 benchmark functions with 10, 20, 30, and 50 variables. it is deduced that hybridization has presented a better performance in optimum seeking from both time and accuracy points of view, which become more distinctive as the number of variables grows. finally, the sum of run times, minimum value of cost functions, and the number of iterations obtained from the procedure of optimization of all functions using the considered algorithms are illustrated in four graphs for each number of variables, which prove the success of the proposed hybrid algorithms.
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کلیدواژه
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hybrid algorithm ,exchange market algorithm ,queen bee algorithm ,shuffled complex evolution
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آدرس
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university of tabriz, faculty of electrical and computer engineering, iran, carleton university, department of electronics, canada, university of tabriz, faculty of electrical and computer engineering, iran
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پست الکترونیکی
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aliasgarlotfi11@gmail.com
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integrating exchange market, queen bee, and shuffled complex evolution algorithms for multi-variable function optimization
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Authors
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salim mina ,hamedifar sima ,lotfi ali asghar
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Abstract
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in this paper, three popular algorithms, including the exchange market algorithm (ema), the shuffled complex evolution (sce) algorithm, and the queen bee (qb) algorithm, are considered to propose three new hybrid evolutionary algorithms named ema-qb, ema-sce, and ema-sce-qb. then, to analyze and validate the effectiveness and efficiency of these new algorithms, we compared their performance with the performance of ema, sce, and qb algorithms on 12 benchmark functions with 10, 20, 30, and 50 variables. it is deduced that hybridization has presented a better performance in optimum seeking from both time and accuracy points of view, which become more distinctive as the number of variables grows. finally, the sum of run times, minimum value of cost functions, and the number of iterations obtained from the procedure of optimization of all functions using the considered algorithms are illustrated in four graphs for each number of variables, which prove the success of the proposed hybrid algorithms.
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