基于正态分布和自适应变异算子的ε截断算法

doi:10.6040/j.issn.1672-3961.0.2018.194

Abstract

Abstract:

It was hard for constrained optimization algorithm to maintain a good balance of convergence and distribution. To solve this problem, a ε-truncation algorithm based on the normal distribution crossover (NDX) and adaptive mutation operator was proposed, which introduced the normal distribution into the simulated binary crossover (SBX) operator, so that the algorithm could search wider space and easily jump out of local optimum. The proposed algorithm combined the adaptive mutation operator with the current information of the individual in the population, and guided the population evolving toward the real Pareto front. Moreover, it preserved the Pareto optimal solutions and a certain number of infeasible solutions with the adaptive ε truncation strategy. At the same time using the information of these infeasible solutions, it increased the search intensity of space and improved the diversity of population. According to three standard test functions experimental results, the solution set in this study could well track the real Pareto solution set. The proposed method could effectively coordinate the convergence and distribution of the algorithm.

Key words: constrained, NDX operator, adaptive mutation operator, adaptive εtruncation strategy

CLC Number:

TP273

Jin LI,Erchao LI. Epsilon truncation algorithm based on NDX and adaptive mutation operator[J].Journal of Shandong University(Engineering Science), 2019, 49(2): 47-53.

Figures/Tables 10

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测试函数	SP				IGD
	NDXAMAENSGA-Ⅱ		FRNSGA-Ⅱ		NDXAMAENSGA-Ⅱ		FRNSGA-Ⅱ
	均值	方差	均值	方差	均值	方差	均值	方差
SRN	0.093 2	0.001 4	1.176 2	0.072 3	0.002 8	0.002 5	0.022 3	0.028 6
BNH	0.421 4	0.050 6	0.802 4	0.049 8	0.009 3	0.007 1	0.038 5	0.018 9
CON	0.039 7	0.078 6	0.078 5	1.265 8	0.010 5	0.020 7	0.076 4	0.124 6

Epsilon truncation algorithm based on NDX and adaptive mutation operator

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