A Hybrid Optimization Technique Using Exchange Market and Genetic Algorithms

This paper proposes a hybrid optimization technique combining genetic and exchange market algorithms. These algorithms are two evolutionary algorithms that facilitate finding optimal solutions for different optimization problems. The genetic algorithm's high execution time decreases its efficie...

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Vydané v:	IEEE access Ročník 8; s. 2417 - 2427
Hlavní autori:	Jafari, Amirreza, Khalili, Tohid, Babaei, Ebrahim, Bidram, Ali
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Approximation algorithms Evolutionary algorithm Evolutionary algorithms Evolutionary computation exchange market algorithm (EMA) Exchanging genetic algorithm (GA) Genetic algorithms Heuristic algorithms hybrid algorithm Linear programming objective function Optimization optimization algorithm Optimization techniques Search algorithms Securities markets Solution space Stock markets
ISSN:	2169-3536, 2169-3536
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Abstract	This paper proposes a hybrid optimization technique combining genetic and exchange market algorithms. These algorithms are two evolutionary algorithms that facilitate finding optimal solutions for different optimization problems. The genetic algorithm's high execution time decreases its efficiency. Because of the genetic algorithm's strength in surveying solution space, it can be combined with a proper exploitation-based algorithm to improve the optimization efficiency. The exchange market algorithm is an optimization algorithm that can effectively find the global optimum of the objective functions in an efficient manner. According to the trade's inherent situation, the stock market works under unbalanced and balanced modes. In order to gain maximum profit, shareholders take specific decisions based on the existing conditions. The exchange market algorithm has two searching and two absorbent operators for acquiring the best-simulated form of the stock market. Simulations on twelve benchmarks with the different dimensions and variables prove the effectiveness of this algorithm compared to eight optimization algorithms.
AbstractList	This paper proposes a hybrid optimization technique combining genetic and exchange market algorithms. These algorithms are two evolutionary algorithms that facilitate finding optimal solutions for different optimization problems. The genetic algorithm's high execution time decreases its efficiency. Because of the genetic algorithm's strength in surveying solution space, it can be combined with a proper exploitation-based algorithm to improve the optimization efficiency. The exchange market algorithm is an optimization algorithm that can effectively find the global optimum of the objective functions in an efficient manner. According to the trade's inherent situation, the stock market works under unbalanced and balanced modes. In order to gain maximum profit, shareholders take specific decisions based on the existing conditions. The exchange market algorithm has two searching and two absorbent operators for acquiring the best-simulated form of the stock market. Simulations on twelve benchmarks with the different dimensions and variables prove the effectiveness of this algorithm compared to eight optimization algorithms.
Author	Babaei, Ebrahim Khalili, Tohid Bidram, Ali Jafari, Amirreza
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SubjectTerms	Approximation algorithms Evolutionary algorithm Evolutionary algorithms Evolutionary computation exchange market algorithm (EMA) Exchanging genetic algorithm (GA) Genetic algorithms Heuristic algorithms hybrid algorithm Linear programming objective function Optimization optimization algorithm Optimization techniques Search algorithms Securities markets Solution space Stock markets
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Title	A Hybrid Optimization Technique Using Exchange Market and Genetic Algorithms
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