Diversity enhanced particle swarm optimizer for global optimization of multimodal problems

This paper presents a diversity enhanced particle swarm optimizer (DivEnh-PSO) which uses an external memory to enhance the diversity of the swarm and to discourage premature convergence. The external memory holds selected past solutions with good diversity. Selected past solutions are periodically injected into the swarm. This approach does not require additional function evaluations as past solutions are used to enhance diversity. Experiments were conducted on multimodal and composition test problems with and without coordinate rotations. The test results indicate improved performance of the DivEnh-PSO in solving multimodal problems when compared with the same PSO implementation without diversity enhancement.