Abstract—In this paper, the hybrid moth-flame and salp swarm algorithm is proposed to enhance the performance of the original moth-flame algorithm. As a moth moves spirally around a flame, the chance of investigating a distance between the moth and the flame is reduced. This paper attached a salp chain to each moth in order to investigate with such distance. The proposed algorithm was evaluated on benchmark functions and compared to the original Moth-Flame optimization algorithm and Particle Swarm Optimization algorithm. The results showed that the proposed algorithm could generate better minimum fitness value up to almost 100% for most functions compared to other algorithms. Moreover, the convergence rate of the proposed algorithms converged to a global optimum faster for most functions compared to other algorithms.
Index Terms—Moth-flame optimization, salp swarm algorithm, hybrid moth-flame and salp swarm algorithm.
The authors are with the Computer Engineering Department, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand (e-mail: {orachun.udo, khajonpong.akk, tiranee.ach}@mail.kmutt.ac.th).
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Cite: Orachun Udomkasemsub, Khajonpong Akkarajitsakul, and Tiranee Achalakul, "Hybrid Moth-Flame and Salp Swarm Optimization Algorithm," International Journal of Modeling and Optimization vol. 9, no. 4, pp. 223-229, 2019.
