Abstract: A multi-objective PSO algorithm based on escalating strategy was proposed. The main idea of this escalating strategy is to regenerate 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. I3y this way, the performance on global convergence can be enhanced, and premature can be avoided simultaneously. A neighborhood search procedure was 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 were analyzed 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 were also investigated to show that the EMPSO with random inertia weight factor has some advantage over than that of fixed inertia weight.

Key words: Escalation evolution, Multi-objective optimization, PSO algorithm, Random inertia weight