AFSA-WOA Variants for Enhanced Global Optimization
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Abstract
Imbalanced exploitation in Metaheuristic Artificial Fish Swarm Algorithm (AFSA) inhibits it from producing good optimization performance. Therefore, this paper presents the proposal of a simple, yet improved AFSA variant for optimization, inspired by combining it with the Whale Optimization (WOA) algorithm. Originally, the standard AFSA algorithm imitates the hunting behavior of fish swarm, while the standard WOA algorithm imitates the whale hunting behavior in a natural environment. In this work, the spiral updating position technique of WOA is incorporated into the swarming and following behaviors of AFSA, creating three new variant algorithms referred to as AFSA-WOA-S, AFSA-WOA-F and AFSA-WOA-SF. The performances of the proposed variants are evaluated based on fifteen benchmark functions. The results have proven that the variants are able to improve the global optimization outputs compared to the standard AFSA and WOA. The best-performed variant among the proposed ones, is AFSA-WOA-F.
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