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.