A novel multivariant optimization algorithm for multimodal optimization

This paper provides a detailed description of a novel multivariant optimization algorithm (MOA) for multi-modal optimization with the main idea to share search information by organizing all search atoms into a special designed structure. Its multiple and variant group property make MOA capable on multi-modal optimization problems. The capability of the MOA method in locating and maintaining multi optima in one execution is discussed in details in this paper and two experiments are carried out to validate its feasibility in multi-modal optimization problems. The experimental results are also compared with those obtained by the species-based PSO, the adaptive sequential niche PSO and the memetic PSO. The experiment results show that MOA has high success rate and convergence speed in multi-modal optimization problems.