An Adaptive Multipopulation Framework for Locating and Tracking Multiple Optima
提出一种自适应多群体框架，通过数据库收集算法行为变化信息并动态调整群体数量，结合多种技术（如启发式聚类、群体休眠等）有效跟踪动态环境中的多个最优点，实验表明在多数场景下优于对比算法。
Multipopulation methods are effective in solving dynamic optimization problems. However, to efficiently track multiple optima, algorithm designers need to address a key issue: how to adapt the number of populations. In this paper, an adaptive multipopulation framework is proposed to address this issue. A database is designed to collect heuristic information of algorithm behavior changes. The number of populations is adjusted according to statistical information related to the current evolving status in the database and a heuristic value. Several other techniques are also introduced, including a heuristic clustering method, a population exclusion scheme, a population hibernation scheme, two movement schemes, and a peak hiding method. The particle swarm optimization and differential evolution algorithms are implemented into the framework, respectively. A set of multipopulation-based algorithms are chosen to compare with the proposed algorithms on the moving peaks benchmark using four different performance measures. The effect of the components of the framework is also investigated based on a set of multimodal problems in static environments. Experimental results show that the proposed algorithms outperform the other algorithms in most scenarios.