Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments

• DOI: 10.48550/arxiv.2201.00523
• Publication Date: 2022-01-01
• Authors: Luo, Wenjian; Lin, Xin; Li, Changhe; Yang, Shengxiang; Shi, Yuhui
• Keywords: FOS: Computer and information sciences; 0202 electrical engineering, electronic engineering, information engineering; Computer Science - Neural and Evolutionary Computing; 02 engineering and technology; Neural and Evolutionary Computing (cs.NE)

Dynamic and multimodal features are two important properties and widely existed in many real-world optimization problems. The former illustrates that the objectives and/or constraints of the problems change over time, while the latter means there is more than one optimal solution (sometimes including the accepted local solutions) in each environment. The dynamic multimodal optimization problems (DMMOPs) have both of these characteristics, which have been studied in the field of evolutionary computation and swarm intelligence for years, and attract more and more attention. Solving such problems requires optimization algorithms to simultaneously track multiple optima in the changing environments. So that the decision makers can pick out one optimal solution in each environment according to their experiences and preferences, or quickly turn to other solutions when the current one cannot work well. This is very helpful for the decision makers, especially when facing changing environments. In this competition, a test suit about DMMOPs is given, which models the real-world applications. Specifically, this test suit adopts 8 multimodal functions and 8 change modes to construct 24 typical dynamic multimodal optimization problems. Meanwhile, the metric is also given to measure the algorithm performance, which considers the average number of optimal solutions found in all environments. This competition will be very helpful to promote the development of dynamic multimodal optimization algorithms.