Multi-objective genetic algorithm based on escalating strategy

Multi-objective genetic algorithms are a kind of probabilistic optimization methods which concern with finding out a uniformly distributed non-inferior solution frontier to a given multi-objective optimization problem. A multi-objective genetic algorithm based on escalating strategy (EMGA) is proposed in this paper. The main idea of this escalating strategy is to re-generate the whole evolutionary population with some technology, which results in a new population significantly indifferent from the old one while inheriting the evolutionary information from the history. By this way, the performance on global convergence can be enhanced, and premature can be avoided simultaneously. A Pareto-ranking based selection strategy is used to reduce the computational expense of the algorithm, and a neighborhood search procedure is imposed on some selected Pareto solutions to accelerate the evolution process for reaching a global Pareto set with well distribution. Some typical multi-objective optimization test problems are taken to solve with EMGA, NSGA and MOGLS respectively to verify the effectiveness of the new algorithm. The details about how to select appropriate escalating parameters and their effect on the performance of EMGA are also investigated.