关键词: 多目标人工蜂群算法, 高维多峰函数, knee points, 自适应识别策略

Abstract: There exist some problems that the traditional Artificial Bee Colony algorithm（ABC） and its extension in multi object（MOABC） has a slow convergence speed, easily falling into local minima, optimization accuracy lost and other issues under the condition of high dimension, multi peak function. Based on the characteristic of knee points that it can improve convergence and distribution, an algorithm that rapidly identificates the knee points is designed in this paper and applied to the MOABC, it proposes the improved Multi-Objective Artificial Bee Colony algorithm based on the strategy of Knee point（KnMOABC）. In the iterative process, the pareto dominating relation is taken into account firstly, and the knee points are selected as the individuals for next generation, which greatly enhances the convergence speed of the algorithm, at the same time, an adaptive strategy is added into the knee point recognition algorithm to ensure the distributivity of the algorithm. The experimental results show that the performance of KnMOABC is better than that of the three latest multi-objective artificial bee colony algorithm.

Key words: multi-objective artificial bee colony algorithm, high-dimensional and multimodal functions, knee points, adaptive identification strategy