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Dynamic-PSO: An improved particle swarm optimizer

In this paper, a variant of particle swarm optimization (PSO) is presented to handle the problem of stagnation encounters in PSO which may lead to get it trapped in local optima and premature convergence particularly in multimodal problems. The proposed scheme Dynamic-PSO (DPSO) does not disturb the fast convergence characteristics of PSO by keeping the basic concept of PSO unaffected. When particles personal best and swarm's global best position do not improve in successive generation i.e. start stagnating DPSO provides dynamicity to particles externally in such a manner that stagnated particles move towards potentially better unexplored region to maintain diversity as this increases chance to recover from stagnation. By identifying and curing stagnated particles, it also avoids the problems of getting trapped in local optima and premature convergence. We have compared the proposed algorithm DPSO with basic PSO and its widely accepted variants over 24 benchmark functions provided by Black-Box Optimization Benchmarking (BBOB 2013). Results show that the proposed variant performs better in comparison with other peer algorithms.