求解约束优化问题的偏好多目标进化算法

doi:10.3969/j.issn.1001-2400.2014.01.018

Abstract

Abstract:

Constrained optimization problems (COPs) are converted into the bi-objective optimization problem and solved with a new preference based multi-objective evolutionary algorithm. A new hybrid crossover operator is proposed to improve the search ability in the evolutionary process, and also a novel fitness function with preference based on the achievement scalarizing function (ASF) which is used in the method of weighted metrics in multi-objective optimization is presented. The new fitness measures the merits of individuals by the weighting distance from individuals to the reference point, where the reference point and the weighting vector afford the preference for selection. In different evolutionary stages, the reference point and weighting vector are chosen adaptively according to the individuals in population to make a tradeoff between the preferences to the two objectives. Numerical experiments for several standard test functions with different characteristics illustrate that the new proposed algorithm is effective and efficient.

Key words: constrained optimization, multi-objective optimization, evolutionary algorithm, preference, achievement scalarizing function

CLC Number:

TP301. 6

DONG Ning;WANG Yuping. Multi-objective evolutionary algorithm based on preference for constrained optimization problems[J].J4, 2014, 41(1): 98-104+188.

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Multi-objective evolutionary algorithm based on preference for constrained optimization problems

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