Evolutionary algorithms (EAs) have been shown to be powerful tools for complex optimization problems, which are ubiquitous in both communication and big data analytics. This paper presents a new EA, namely negatively correlated search (NCS), which maintains multiple individual search processes in parallel and models the search behaviors of individual search processes as probability distributions. NCS explicitly promotes negatively correlated search behaviors by encouraging differences among the probability distributions (search behaviors). By this means, individual search processes share information and cooperate with each other to search diverse regions of a search space, which makes NCS a promising method for nonconvex optimization. The co-operation scheme of NCS could also be regarded as a novel diversity preservation scheme that, different from other existing schemes, directly promotes diversity at the level of search behaviors rather than merely trying to maintain diversity among candidate solutions. Empirical studies showed that NCS is competitive to well-established search methods in the sense that NCS achieved the best overall performance on 20 multimodal (nonconvex) continuous optimization problems. The advantages of NCS over state-of-the-art approaches are also demonstrated with a case study on the synthesis of unequally spaced linear antenna arrays. © 1983-2012 IEEE.
Bibliographical noteThis work was supported in part by the National Natural Science Foundation of China (Grant 61329302 and Grant 61175065), in part by the Program for New Century Excellent Talents in University (Grant NCET-12-0512), in part by the EPSRC (Grant EP/J017515/1).
- Diversity Maintenance
- Evolutionary Algorithms
- Negative Correlation
- Optimization in Communication