Hybrid multi-objective genetic algorithm for multi-objective optimization problems

As a result of important practical significance in real-world engineering applications, multi-objective optimization problem has been one of scientific problems concerned by many researchers. In recent years, genetic algorithm (GA) has begun to be widely used to solve a variety of multi-objective optimization problems due to its population-based search mechanism. In this paper, NSGA-II, which is a most classical multi-objective GA, is investigated and discussed in detail. In order to address the problem of exploitation lacking in the search process of NSGA-II, a local search strategy, which is able to applied in multi-objective optimization domain, is proposed and led into NSGA-II efficiently. Based on a set of benchmark test functions, the experimental results show that the proposed algorithm has demonstrated superior to NSGA-II in terms of convergence and distribution.