Enhanced Strength Pareto Differential Evolution (ESPDE): An Extension of Differential Evolution for Multi-objective Optimization

As a simple but powerful evolutionary optimization algorithm, differential evolution (DE) is paid wide attention and research in both academic and industrial fields and successfully applied to many real-world optimization problems. In recent years, several multi- objective optimization algorithms based on DE have been proposed to solve multi-objective optimization problems (MOPs). In this paper, a novel extension of DE for MOPs---enhanced strength Pareto differential evolution (ESPDE), is described. The reason why we call it ESPDE is that it borrows the methods of fitness assignment and density estimation used by improved strength pareto evolutionary algorithm (SPEA2), furthermore, an adaptive Gauss mutation (AGM) based on dimension is added in ESPDE to avoid premature convergence. Simulation results on several difficult test problems and the comparisons with other multi-objective algorithms show that ESPDE is effective and robust.

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