Interactive Self Improvement Based Adaptive Particle Swarm Optimization

作者: S. B. Vinay Kumar , P. V. Rao

DOI: 10.1080/13614576.2017.1297732

关键词:

摘要: ABSTRACTOne of the most familiar stochastic heuristic search algorithm is Particle swarm optimization (PSO), which motivated by social behavior animals like birds, fishes, and so forth. The significant advantages PSO are simple structure limited parameters to be used. Among parameters, inertia weight considered as crucial one in brings trade-off between characteristics exploitation exploration. A novel Interactive Self-Improvement based Adaptive (ISI-APSO) method that traits better searching efficiency accuracy than traditional particle proposed. More precisely, it can achieve faster convergence speed while on global over entire space. simulation results show performance our proposed ISI-APSO substantially improved other algorithms terms speed.

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