论文标题
基于NICHING指标的多模式多模式多目标优化器
A Niching Indicator-Based Multi-modal Many-objective Optimizer
论文作者
论文摘要
多模式多目标优化是在尽可能多的（几乎）等效的帕累托最佳解决方案定位（几乎）。文献中已经提出了一些用于多模式多目标优化的进化算法。但是，没有有效的多模式多模式多目标优化的方法，其中目标数量超过三个。为了解决这个问题，本文提出了一种基于易害指标的多模式多模式多模式和多目标优化算法。在提出的方法中，在解决方案空间中的儿童及其最接近的个体之间进行健身计算以维持多样性。在多模式的多目标测试问题上评估提出的方法的性能，最多15个目标。结果表明，所提出的方法可以处理大量目标，并找到多个等效帕累托最佳解决方案的良好近似值。结果还表明，所提出的方法的性能明显优于八种多目标进化算法。
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi- and many-objective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms.
