MOEA/D聚合函数的二次泛化及其优化性能分析

doi:10.12052/gdutxb.170147

Abstract

Abstract: Multi-objective Evolutionary Algorithm Based on decomposition (MOEA/D) is an important branch. Achieving balance between convergence and diversity is a key issue in evolutionary multi-objective optimization. There are more or less deficiencies and shortcomings in the mainstream scalarizing functions. When using Tchebycheff to choose individuals, individuals often deviate from the weight and can not combine well with weight. On this basis, a new scalarizing function which improves performance of MOEA/D is presented. The scalarizing function is a quadratic function and its contour line is also a quadratic function (In this paper it is called Hyperbola function method, which is HYB.), which is a generalization to the current scalarizing functions. Comparing with PBI, this algorithm has better convergence and the balance between convergence and diversity is easily obtained. After testing MOKP and series of DTLZ and comparing with other algorithms, HYB is shown to be stable and effective and to improve the speed of convergence.

Key words: multi-objective optimization, multi-objective evolutionary algorithm based on decomposition (MOEA/D), scalarizing functions

CLC Number:

TP301.6

Zhou Yi-lu, Wang Zhen-you, Li Ye-zi, Li Feng. A Quadratic Scalarizing Function in MOEA/D and its Performance on Multi and Many-Objective Optimization[J].Journal of Guangdong University of Technology, 2018, 35(04): 37-44.

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