Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems

I-Ming Chao; Shou-Cheng Hsiung; Jenn-Long Liu

doi:10.46604/aiti.2020.4167

Authors

I-Ming Chao Department of Industrial Management, I-Shou University, Kaohsiung, Taiwan
Shou-Cheng Hsiung Department of Industrial Management, I-Shou University, Kaohsiung, Taiwan
Jenn-Long Liu Department of Information Management, I-Shou University, Kaohsiung, Taiwan

DOI:

https://doi.org/10.46604/aiti.2020.4167

Keywords:

whale optimization algorithm, bubble-net feeding method, inertia weights, exploration and exploitation capabilities

Abstract

Whale Optimization Algorithm (WOA) is a new kind of swarm-based optimization algorithm that mimics the foraging behavior of humpback whales. WOA models the particular hunting behavior with three stages: encircling prey, bubble-net attacking, and search for prey. In this work, we proposed a new linear decreasing inertia weight with a random exploration ability (LDIWR) strategy. It also compared with the other three inertia weight WOA (IWWOA) methods: constant inertia weight (CIW), linear decreasing inertia weight (LDIW), and linear increasing inertia weight (LIIW) by adding fixed or linear inertia weights to the position vector of the reference whale. The four IWWOAs are tested with 23 mathematical and theoretical optimization benchmark functions. Experimental results show that most of IWWOAs outperform the original WOA in terms of solution accuracy and convergence rate when solving global optimization problems. Accordingly, the LDIWR strategy produces a better balance between exploration and exploitation capabilities for multimodal functions.

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