A Hybrid Evolutionary Approach to Solve Multi-objective Optimization Problems based on Particle Swarm Optimizer and Genetic Algorithm

Handling multi-objective optimization problems using evolutionary computations represents a promising interest area of research, especially the hybrid evolutionary computations. In multi-objective optimization problems the decision maker is interested in determining the set of Pareto-optimal solutions instead of single solution. This paper presents a hybrid evolutionary approach to solve this class of problems. The approach combined two attractive evolutionary techniques, Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA) to enhance the search process by improving the diversity, and the convergence toward the preferred solution. The proposed approach can be implemented for solving both linear and non linear multi-objective optimization problems. The approach efficiency is verified and tested using a set of multimodal functions commonly used as benchmark optimization problems in evolutionary computation. Results indicate that the approach is highly competitive and can be considered as a viable alternative to solve multi-objective optimization problems.