A multi-objective PSO algorithm based on escalating strategy

In this paper, a multi-objective PSO algorithm based on escalating strategy will be proposed. 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 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 escalation PSO and non-escalation PSO 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 EMPSO are also investigated to show that the EMPSO with random inertia weight factor has some advantage over than that of fixed inertia weight.