Birmingham Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators
Birmingham Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators
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发表时间：
2016
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通讯作者：
Bingdong Li;K. Tang;Jinlong Li;X. Yao
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作者：
Bingdong Li;K. Tang;Jinlong Li;X. Yao
——传统的多目标进化算法在处理多个目标时面临着巨大的挑战。这是由于总体中非支配解的比例很高，并且帕累托前沿的选择压力较低。为了解决这个问题，人们提出了一系列基于指标的算法来引导搜索过程走向帕累托前沿。然而，单一指标可能会产生偏差，并导致人口聚集到帕累托前沿的某个子区域。本文针对多目标优化问题提出了一种基于多指标的算法。所提出的算法，即基于随机排序的多指标算法（SRA），采用随机排序技术来平衡不同指标的搜索偏差。对来自两个明确定义的具有 5、10 和 15 个目标的基准集的大量（总共 39 个）问题实例进行的实证研究表明，与最先进的算法相比，SRA 在倒代距离和超体积指标方面表现良好。实证研究还表明，在问题需要算法具有较强收敛能力的情况下，通过结合基于方向的存档来存储收敛性良好的解并保持多样性，可以进一步提高SRA的性能。
—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.