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An improved multi-population whale optimization algorithm
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AI
DOI：10.1007/s13042-022-01537-3.png)
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Abstract
En 中文
Clustering techniques and metaheuristic algorithms (MA) have demonstrated being efficient tools in their respective action fields. However, working together is an area marginally explored. One of the main disadvantages of MA is the lack of diversity in the solutions. Besides, most of them use only a single population to analyze the search space; this affects the capabilities to find the optimal solutions. This article proposes an approach called K-WOA that merges the benefits of two methods into a single algorithm. The K-means is a popular clustering technique based on centroids. Due to its simplicity and efficiency, combining it with a MA as the whale optimization algorithm (WOA) is ideal. This proposed K-WOA aims to increase the diversity of solutions in optimization problems by creating multiple groups of search agents operating cooperatively to explore the search space. To perform this task, the K-means is used in the initialization process to separate the population into different subgroups that the WOA independently evolves. In each sub-population, the best search agent is chosen to compare with the best agents of the other whale groups. By doing this, the algorithm can explore different regions of the search space simultaneously with more than one element. The K-WOA is proposed as an improved optimization algorithm that simultaneously searches for optimal solutions in multiple regions of the search space. The experimental results and comparisons with state-of-the-art approaches show that the proposed algorithm is competitive for solving complex optimization problems.
Keywords:
Metaheuristic algorithms
Clustering
K-means
Whale optimization algorithm
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