Abstract
In this study we present an extension to the PSOGP algorithm for multimodal optimization problems. PSOGP avoids premature convergence by utilizing a method wherein the swarm is made more diverse by employing a mechanism which allows each particle to use a different equation to update its velocity. This equation is also continuously evolved through the use of genetic programming to ensure adaptability. Enhancements have been proposed which make PSOGP suitable for finding solutions to multimodal optimization problems. We propose a partial random initialization strategy and a generation gap strategy. We also suggest the use of speciation which enables PSOGP to locate multiple solutions. We compare the performance of SPSOGP with SPSO and NichePSO on 5 multimodal test functions.