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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Vydáno v:IEEE access Ročník 8; s. 2417 - 2427
Hlavní autoři: Jafari, Amirreza, Khalili, Tohid, Babaei, Ebrahim, Bidram, Ali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2962153