Abstract
This paper introduces a novel variant of PSO called accelerated exploration particle swarm optimizer (AEPSO). The AEPSO algorithm select the particles that are far away from the global solution and accelerates them towards global optima with an exploration power to avoid the premature convergence. The performance comparisons such as search efficiency, quality of solution and algorithmic complexity of the proposed algorithm are provided against different high performance PSOs. The comparison is carried out on the set of 30 and 50 dimensional complex multimodal benchmark functions with and without coordinate rotation. Simulation results indicate that the proposed algorithm gives robust results with good quality solution and faster convergence.
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