关键词: 差分进化, 可控支配域, 主成分分析, 基于分解的多目标进化算法

Abstract: To solve the shortcomings of low accuracy and low speed in convergence of MOEA/D’s Differential Evolution（DE）, a novel DE model based on Controlling Dominance Area of Solutions（CDAS） and dynamic scaling factor is proposed. It can guide explicit and implicit search. And an improved MOEA/D-iDE algorithm based on a new differential evolution model is realized. The new DE model classifies the individuals of the neighborhood by non-dominated sorting of CDAS and produces a vector difference of evolutionary direction to achieve explicit convergence speed guide in the different evolutionary period. Analysing decision space by PCA, the scaling factor is adjusted dynamically to achieve the implicit convergence accuracy guide. The MOEA/D-iDE algorithm is compared with 6 optimization algorithms in IGD+ and ER indicators by testing ZDT, DTLZ and WFG benchmarks. The experimental results show that MOEA/D-iDE has a better performance in speed and accuracy of convergence than other algorithms, and validate the effectiveness of the new DE model.

Key words: differential evolution, controlling dominance area of solutions, principal component analysis, Multi-Objective Evolutionary Algorithm based on Decomposition（MOEA/D）