Hybrid PSO Algorithm for the Solution of Learningbased Real-Parameter Single Objective Optimization Problems

During the past 20 years, the community of science have become more interested in Evolutionary Algorithms which have been used in many applications. This thesis proposes hybridized Particle Swarm Optimization (PSO) algorithm that targets to combine the original PSO with a simple local search technique (HPSO-FminLS). FminLS, have been used as a simple local search with original PSO for solving Learning-based-Real-Parameter Single Objective Optimization Problems (LbRPSOOP). These problems are provided in CEC2015 Congress on Evolutionary Computation. Technically, we solved CEC15 in dimensions D10, D30, D50 with HPSO-FminLS then developed 4 different versions by using local search and PSO algorithms. HPSO-FminLS reached optimal solution in Unimodal problems, and the near optimal solution in other problems.