Negatively Correlated Search


Evolutionary algorithms (EAs) have been shown to be powerful tools for complex optimization problems, which are ubiquitous in each Communication and massive information analytics. This paper presents a replacement EA, particularly negatively correlated search (NCS), which maintains multiple individual search processes in parallel and models the search behaviors of individual search processes as likelihood distributions. NCS explicitly promotes negatively correlated search behaviors by encouraging differences among the chance distributions (search behaviors). By this suggests, individual search processes share info and cooperate with each different to go looking diverse regions of a search house, that makes NCS a promising methodology for nonconvex optimization. The co-operation scheme of NCS may conjointly be regarded as a completely unique diversity preservation scheme that, completely different from different existing schemes, directly promotes diversity at the extent of search behaviors instead of just attempting to keep up diversity among candidate solutions. Empirical studies showed that NCS is competitive to well-established search ways in the sense that NCS achieved the most effective overall performance on twenty multimodal (nonconvex) continuous optimization problems. The benefits of NCS over state-of-the-art approaches are also demonstrated with a case study on the synthesis of unequally spaced linear antenna arrays.

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