Constrained self regulating particle swarm optimization

Self regulating particle swarm optimization (SRPSO) is a variant of (PSO) which has proved to be very efficient algorithm for unconstrained compared with other evolutionary algorithms (EAs) and utilized recently by the researchers solving real-world problems. However, SRPSO not been evaluated analyzed constrained optimization. In this work, exhaustively using 24 benchmark problems coupling it four constraint handling techniques (CHTs). The results have two i.e. Differential evolution (DE) PSO. DE PSO also coupled same CHTs on Statistical analysis performance evaluation three shows that better than but found deficient when 95% confidence level. Therefore, objective work evaluate comprehensively different views come up suitability particular CHT specific type