Efficient Hybrid Multi-Objective Evolutionary Algorithm
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In the artificial intelligence community the multi-objective optimization problem become very common and has been rapidly increasing attention. This significant is due to the fact that there is high number of real-world applications having optimization problems that include more than one objective function. As has been evident in the last ten years, the evolutionary algorithms are one of the best choices to solve multi-objective optimization problems. Although evolutionary algorithms are the most common approach to solve multi-objective optimization problems, there is still many issues and drawbacks that need solving and enhancing. In this paper a set of improved hybrid Memetic evolutionary algorithms are proposed to solve multi-objective optimization problems. The proposed algorithms enhance the performance of NSGA-II algorithm by using different new proposed and simple search schemes. Merging a simple and efficient search technique to NSGA-II significantly enhances the convergence ability and speed of the algorithm. To assess the performance of proposed algorithms, three multi-objective test problems are used from ZDT set. Our empirical results in this paper show that the proposed algorithms significantly enhance the NSGA-II algorithm performance in both diversity and convergence.