Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators
Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators
复制标题
基于多指标的多目标优化随机排序算法
DOI：
10.1109/tevc.2016.2549267
复制
发表时间：
2016-12-01
影响因子：
14.3
通讯作者：
Yao, Xin
中科院分区：
文献类型：
--
作者：
Li, Bingdong;Tang, Ke;Yao, Xin
传统的多目标进化算法在处理多目标问题时面临着巨大的挑战。这是由于群体中非劣势解的比例很高，并且帕累托前沿的选择压力很低。为了解决这个问题，一系列基于指标的算法已经被提出来引导搜索过程向帕累托前沿。然而，一个单一的指标可能是有偏差的，并导致人口向帕累托前沿的一个次区域集中。针对多目标优化问题，提出了一种基于多指标的优化算法。该算法采用随机排序技术来平衡不同指标的搜索偏差，称为基于随机排序的多指标算法（SRA）。大量（共39）的问题实例的实证研究，从两个定义良好的基准集与5，10，和15个目标表明，SRA表现良好，与国家的最先进的算法相比，在倒置的世代距离和超体积指标。实证研究还表明，在一个问题，要求算法具有较强的收敛能力的情况下，SRA的性能可以进一步提高，通过纳入一个基于方向的档案存储良好收敛的解决方案，并保持多样性。
Traditional multiobjective evolutionary algorithms face a great challenge when dealing with many objectives. This is due to a high proportion of nondominated solutions in the population and low selection pressure toward the Pareto front. In order to tackle this issue, a series of indicator-based algorithms have been proposed to guide the search process toward the Pareto front. However, a single indicator might be biased and lead the population to converge to a subregion of the Pareto front. In this paper, a multi-indicator-based algorithm is proposed for many-objective optimization problems. The proposed algorithm, namely stochastic ranking-based multi-indicator Algorithm (SRA), adopts the stochastic ranking technique to balance the search biases of different indicators. Empirical studies on a large number (39 in total) of problem instances from two well-defined benchmark sets with 5, 10, and 15 objectives demonstrate that SRA performs well in terms of inverted generational distance and hypervolume metrics when compared with state-of-the-art algorithms. Empirical studies also reveal that, in the case a problem requires the algorithm to have strong convergence ability, the performance of SRA can be further improved by incorporating a direction-based archive to store well-converged solutions and maintain diversity.