ar-MOEA: A Novel Preference-Based Dominance Relation for Evolutionary Multiobjective Optimization
ar-MOEA: A Novel Preference-Based Dominance Relation for Evolutionary Multiobjective Optimization
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DOI：
10.1109/tevc.2018.2884133
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发表时间：
2019-10
影响因子：
14.3
通讯作者：
Jun Yi;Junren Bai;Haibo He;Jun Peng;Dedong Tang
中科院分区：
文献类型：
--
作者：
Jun Yi;Junren Bai;Haibo He;Jun Peng;Dedong Tang
对于实际应用中的多目标优化问题，寻找总体帕累托最优前沿，同时解决不断增加的目标数的影响，已经成为一个基本的和具有挑战性的问题。决策者提供的偏好信息可以指导搜索帕累托前沿的偏好区域，加速种群的收敛。本文提出了一种新的Pareto优势关系，称为偏好角和基于参考信息的优势，以在非支配解之间建立更严格的偏序。在该方法中，通过计算候选解与参考点之间的欧几里德距离和角度信息来评价算法的收敛程度和种群多样性。此外，还设计了一个自适应门限，用于在预先指定的区间内通过迭代过程调整AR-优势的判断条件。该算法加快了种群的收敛速度，减少了非偏好区域的解数目。针对不同的基准测试问题和实际铝电解生产案例，给出了两种性能指标的对比评估实验。实验结果表明，与现有的五种进化算法相比，该算法对求解高度复杂的多目标优化问题是有效的。
Finding the overall Pareto optimal front while addressing the effect of an increasing number of objectives has become an essential and challenging issue for multiobjective optimization in real-world applications. Preference information provided by a decision maker can guide the search for preferred regions of the Pareto front and accelerate the convergence of the population. In this paper, a new variant of the Pareto dominance relation, called preference angle and reference information-based dominance, is proposed to create a stricter partial order among nondominated solutions. In the proposed method, the Euclidean distance and angle information between candidate solutions and reference points are calculated to evaluate the degree of convergence and population diversity, respectively. In addition, an adaptive threshold is designed to adjust the judgment condition of ar-dominance using an iterative process in a prespecified interval. The proposed algorithm increases the convergence speed of the population and reduces the number of solutions in the nonpreferred region. Comparative evaluation experiments are presented with respect to two performance metrics for a variety of benchmark test problems and real-world aluminum electrolytic production cases. The results demonstrate that the proposed approach is effective for highly complex, multiobjective optimization problems when compared with five state-of-the-art evolutionary algorithms.