Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems
| Autor: | I-Ming Chao, Jenn-Long Liu, Shou-Cheng Hsiung |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Environmental Engineering bubble-net feeding method General Computer Science biology Optimization algorithm Renewable Energy Sustainability and the Environment Whale Computer science media_common.quotation_subject General Engineering Energy Engineering and Power Technology exploration and exploitation capabilities Inertia Management of Technology and Innovation biology.animal lcsh:Technology (General) lcsh:T1-995 whale optimization algorithm Global optimization problem inertia weights media_common |
| Zdroj: | Advances in Technology Innovation (2020) |
| ISSN: | 2518-2994 2415-0436 |
| DOI: | 10.46604/aiti.2020.4167 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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