PopDMMO: A general framework of population-based stochastic search algorithms for dynamic multimodal optimization
Dynamic multimodal optimization problems (DMMOPs) are a class of problems consisting of two characteristics, i.e., dynamic and multimodal natures. The former characteristic reveals that the properties of DMMOPs change over time, which is derived from dynamic optimization problems (DOPs). The latter one indicates that there exist multiple global or acceptable local optima, which comes from the multimodal optimization problems (MMOPs). Although there has been much attention to both DOPs and MMOPs in the field of meta-heuristics, there is little work devoting to the connection between the dynamic and multimodal natures in DMMOPs. To solve DMMOPs, the strategies dealing with dynamic and multimodal natures in the algorithms should cooperate with each other. Before looking deeply into the connections between two natures, there is necessary to measure the performances of the methods dealing with two natures in DMMOPs. In this paper, first, considering the dynamic and multimodal natures of DMMOPs, we design a set of benchmark problems to simulate various dynamic and multimodal environments. Then, we propose the optimization framework called PopDMMO containing several popular algorithms and methods to test and compare the performances of these algorithms, which gives a general view of solving DMMOPs.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Lin. X., Luo, W., Xu, P., Qiao, Y. and Yang, S. (2021) PopDMMO: A general framework of population-based stochastic search algorithms for dynamic multimodal optimization. Swarm and Evolutionary Computation, 101011.
ISSN : 2210-6502
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes
Showing items related by title, author, creator and subject.
Yang, Shengxiang (Article)Recently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal ...
Dynamic optimization approach for solving an optimal scheduling problem in water distribution systems A new dynamic optimization (DO) approach to solve large scale optimal scheduling problems for water distribution networks is presented. The main motivation of this research is to formulate an algorithm which is significantly ...
Optimized Neural Network Using Differential Evolutionary and Swarm Intelligence Optimization Algorithms for RF Power Prediction in Cognitive Radio Network: A Comparative study Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The a priory knowledge of Radio Frequency (RF) power (primary ...