IEEE Access (Jan 2020)

A Hybrid Optimization Technique Using Exchange Market and Genetic Algorithms

  • Amirreza Jafari,
  • Tohid Khalili,
  • Ebrahim Babaei,
  • Ali Bidram

DOI
https://doi.org/10.1109/ACCESS.2019.2962153
Journal volume & issue
Vol. 8
pp. 2417 – 2427

Abstract

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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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