A Novel Multi-Objective Evolutionary Algorithm Based on Linear Programming

2018 14th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2018-11-01 DOI:10.1109/CIS2018.2018.00082

Zhicang Wang, Hecheng Li

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引用次数: 1

Abstract

It is the goal of scholars in the field of multiobjective optimization to find wide distributive and uniform Pareto solution set over Pareto front. The reason is that the solutions of multi-objective optimization problem is a set of Pareto solutions which are non-dominated each other, and the obtained Pareto solutions are often not well distributed and cannot satisfy the needs of decision makers. It may be the case that decision-makers expect to have a solution in an area to assist them for making decisions. In this paper, we propose a local search strategy based on linear programming and construct a multi-objective evolutionary algorithm based on linear programming (MOEA/LP). MOEA/LP algorithm makes up for the large “gap” in Pareto front, and makes Pareto optimal solutions over Pareto front more uniform and more extensive. Thereby, the decision makers use MOEA/LP algorithm to make more effective choice. Experiment results show the proposed algorithm has better performance according to some measure indices such as running time, hypervolue and C metric, etc.

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一种新的基于线性规划的多目标进化算法

在Pareto前沿上寻找广分布且均匀的Pareto解集是多目标优化领域学者研究的目标。原因是多目标优化问题的解是一组彼此非支配的帕累托解，而得到的帕累托解往往分布不好，不能满足决策者的需要。决策者可能期望在某一领域有一个解决方案来帮助他们做出决策。本文提出了一种基于线性规划的局部搜索策略，并构造了一种基于线性规划的多目标进化算法(MOEA/LP)。MOEA/LP算法弥补了Pareto前沿的巨大“缺口”，使得Pareto前沿上的Pareto最优解更加均匀和广泛。因此，决策者使用MOEA/LP算法来做出更有效的选择。实验结果表明，该算法在运行时间、超容量和C度量等度量指标上具有较好的性能。

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2018 14th International Conference on Computational Intelligence and Security (CIS)

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