Abstract
In the category of swarm intelligence based algorithms, Particle Swarm Optimization (PSO) is an effective population-based metaheuristic used to solve complex optimization problems. In PSO, global optima is searched with the help of individuals. For the efficient search process, individuals have to explore whole search space as well as have to exploit the identified search area. Researchers are continuously working to balance these two contradictory properties i.e. exploration and exploitation and have been modified the PSO in many different ways to improve its solution search capability in the search space. In this regard, incorporation of inertia weight strategy in PSO is a significant modification and after that many researchers have been developed different inertia weight strategies to improve the solution search capability of PSO. This paper presents an analysis of the developed inertia weight strategies in respect to problem-solving capability and their effect in the solution search process of PSO. The effect of 30 recent inertia weight strategies on PSO is measured while comparing over ten well known test functions of having different degree of complexity and modularity.
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Rathore, A., Sharma, H. (2017). Review on Inertia Weight Strategies for Particle Swarm Optimization. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-10-3325-4_9
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DOI: https://doi.org/10.1007/978-981-10-3325-4_9
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