Abstract
Multimodal benchmark function optimization has gained a growing interest exclusively in the evolutionary computation research field which involves achieving all or most of the multiple solutions contrasting a single best solution. A large number of real-world optimization problems can be considered as multimodal function optimization. Recently introduced Whale Optimization Algorithm (WOA) algorithm is inspired by the hunting behavior of humpback whales. The performance of WOA is very promising but the robustness and convergence need further improvement. In this paper, ‘step equation’ of Artificial Fish Swarm Algorithm (AFSA) was incorporated to enhance the robustness and convergence of the original WOA considering five multimodal test functions (F1–F5) for global numerical optimization. The proposed variant of WOA showed improved performances compared to original WOA in terms of average best fitness, robustness and convergence.
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Acknowledgements
This research is supported by USM Global Fellowship (USM.IPS/USMGF/2/2016) and the Ministry of Higher Education (MOHE) Malaysia Fundamental Research Grant Scheme (Grant no. FRGS/1/2017/203.PELECT.6071371).
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Rahman, I., Mohamad-Saleh, J., Sulaiman, N. (2019). Artificial Fish Swarm-Inspired Whale Optimization Algorithm for Solving Multimodal Benchmark Functions. In: Zawawi, M., Teoh, S., Abdullah, N., Mohd Sazali, M. (eds) 10th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-13-6447-1_8
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