Solving Multi-Objective Optimization Problems by RasID-GA: Using an External Population in Genetic Operators

This paper proposes an algorithm to solve multi-objective problems by Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) which uses an external population, called pareto vector set P, in genetic operators. RasID is an optimization algorithm, which is good at finding local optima, but its diversified search isn't so efficient. To increase its efficiency, we combined RasID with genetic algorithms (GA), which are superior at finding global optima. In this paper, RasID-GA adapted to solve multi-objective optimization problems aims to find the Pareto-Optimal solutions using a non dominated sorting. The results are compared with the NSGA-II algorithm by simulating well known benchmarks.