Abstract
Particle swarm optimization [PSO] is one of the most accepted optimization algorithm and due to its simplicity it has been used in many applications. Although PSO converges very fast, it has stagnation and premature convergence problem. To improve its convergence rate and to remove stagnation problem, some changes in velocity vector are suggested. These changes motivate each particle of PSO in different directions so that full search space can be covered and better solutions can be captured. Moreover, autotuning of random parameters are done to remove stagnation problem and local optima. This auto-improved version is named as AI-PSO algorithm. The performance of the proposed version is compared with various state-of-the-art algorithms such as PSO-TVAC and basic PSO. Results show the superiority of the algorithm.
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Kumar, A., Singh, B.K., Patro, B.D.K. (2018). Auto Improved-PSO with Better Convergence and Diversity. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_5
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DOI: https://doi.org/10.1007/978-981-10-3773-3_5
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