Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique.

Area of Science:

• Computational intelligence
• Optimization algorithms
• Metaheuristics

Background:

• Real-world problems often require finding multiple global optima, necessitating multimodal optimization approaches.
• The whale optimization algorithm (WOA) is a recognized global search algorithm, but its multimodal capabilities require enhancement.
• Existing single-modal optimization methods differ significantly from multimodal approaches.

Purpose of the Study:

• To propose a novel multimodal version of the whale optimization algorithm (MMWOA).
• To enhance the multimodal search capabilities of the WOA.
• To improve the local search efficiency of the WOA.

Main Methods:

• The proposed MMWOA integrates niching techniques to boost multimodal search.
• Gaussian sampling is combined with MMWOA to refine local search efficiency.
• The algorithm was evaluated on CEC'2013 multimodal benchmark functions and a non-linear constrained problem.

Main Results:

• MMWOA demonstrated competitive performance against state-of-the-art multimodal optimization algorithms.
• Experimental results validated the enhanced multimodal search ability of MMWOA.
• The combination of niching and Gaussian sampling proved effective.

Conclusions:

• MMWOA offers a promising approach for solving multimodal optimization problems.
• The enhanced WOA variant shows significant potential for real-world applications requiring multiple solutions.
• MMWOA provides a competitive alternative to existing multimodal optimization techniques.