Huang Ting, Gong Yue-Jiao, Chen Wei-Neng, Wang Hua, Zhang Jun
IEEE Trans Cybern. 2022 Jan;52(1):51-64. doi: 10.1109/TCYB.2020.2972907. Epub 2022 Jan 11.
Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms: 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions.
多模态优化问题有多个满意解需要识别。现有的大多数工作都是基于当前种群的信息进行搜索，这可能效率低下。本文提出了一种概率小生境进化计算框架，该框架基于更充分的历史信息来指导未来的搜索，以便找到多样化的高质量解。构建了一个二进制空间划分树来结构化地组织空间访问信息。基于该树，定义了一种概率小生境策略，通过充分利用结构化的历史信息来加强探索和利用。所提出的框架对于纳入各种基线小生境算法具有通用性。在本文中，我们将所提出的框架与两种小生境算法集成：1）基于距离的差分进化算法和2）基于拓扑的粒子群优化算法。在20个多模态优化测试函数上对这两种新算法进行了评估。实验结果表明，所提出的框架有助于算法获得有竞争力的性能。在大多数测试函数上，它们优于许多现有的先进小生境算法。
