Negatively Correlated Search with Asymmetry for Real-Parameter Optimization Problems

As many real-world applications are closely related to complex real-parameter optimization problems, some metaheuristic assumptions are employed to help design search strategies and have been shown to be powerful tools. The balance between exploration (diversification) of new areas of the search space and exploitation (intensification) of good solutions accomplished by this kind of algorithms is one of the key factors for their high performance with respect to other metaheuristics. In particular, negatively correlated search (NCS) improves the search performance of parallel hill climbing by introducing negative correlation of search trends between search processes, which contributes greatly to the diversity maintenance of solutions. NCS models the search behaviors of individual search processes as probability distributions. On this basis, we further divide the search behaviors of a couple of search processes into global search behavior and local search behavior according to the size of the coverage of each search process. Then we present a new metaheuristic, namely negatively correlated search with asymmetry (NSA), which assumes that the search process with global search behavior should be away from the search process with local search behavior. Due to the asymmetry of the negative correlation between search processes, the efficiency of NSA has been greatly improved compared with NCS. The experimental results show that NSA is competitive to well-established search methods in the sense that NSA achieves the best overall performance on 20 multimodal real-parameter optimization problems.