Remora Optimization Algorithm Combining Joint Opposite Selection and Host Switching

• Main Authors: JIA Heming, WEN Changsheng, WU Di, RAO Honghua, LIU Qingxin, LI Shanglong
• Format: Article in Journal/Newspaper
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2023
• Subjects: remora optimization algorithm; meta heuristic optimization algorithm; joint opposite selection; host switching mechanism; beluga whale optimization; benchmark function test; engineering problem solving; Electronic computers. Computer science; QA75.5-76.95; Roa; Beluga; Beluga whale; Beluga*
• Online Access: https://doi.org/10.3778/j.issn.1673-9418.2210057; https://doaj.org/article/9a528c49de254653a75c941e7f2fd453

The remora optimization algorithm (ROA) is a meta heuristic optimization algorithm proposed in 2021. It simulates the behavior of parasitic attachment to the host, empirical attack and host foraging in the ocean. The structure of ROA is simple and easy to implement, but the overall situation is slightly insufficient, which easily leads to ROA’s slow convergence speed and even difficult convergence in the later period. To solve the above problems, host switching mechanism is added in the exploration phase, and new host beluga is introduced to improve the exploration ability of original ROA. At the same time, through adding joint opposite selection strategy, the ability of the algorithm to jump out of the local optimum is enhanced, and the comprehensive optimization performance of the algorithm is further improved. Through the above improvements, an improved remora optim-ization algorithm (IROA) is proposed, which integrates the joint opposite selection and host switching mechanism. In order to verify the performance and improvement advantages of IROA, IROA is compared with the original ROA, six typical original algorithms and four improved algorithms on ROA. Experimental results of CEC2020 standard test function show that IROA has stronger optimization ability and higher convergence accuracy. Finally, the advantages and engineering applicability of the improved algorithm are further verified by solving the car crashworthiness design problem.