Optimization of Multimodal Benchmark Functions Using Fish Cooperative Hunting Behaviors

The rising complexity of real-world problems has inspired researchers to search for effective problem-solving techniques. Swarm intelligence and evolutionary computation techniques are outstanding examples that nature has been an unending source of motivation. Bio-inspired optimization algorithms stem from the major field of computational Intelligence (CI) domains that adopt their working principle from numerous natural phenomena particularly aimed at optimization. Recently introduced Whale Optimization Algorithm (WOA) is motivated by the special hunting mechanism of humpback type whales. The performance of WOA is very promising but the robustness and convergence need further enhancement. In this study, authors modified the original Whale Optimization Algorithm (WOA) by incorporating the ‘step equation’ of Artificial Fish Swarm Algorithm (AFSA) to improve the convergence and robustness of the standard whale algorithm and tested on three popular multimodal benchmark functions (Schwefel, Rastrigin and Griewank). Finally, authors provide few open proposals with a certain concern of being answered in future.