Hybrid Multi-population Collaborative Asynchronous Search

The paper explores connections between population topology and individual interactions inducing autonomy, communication and learning. A Collaborative Asynchronous Multi-Population Evolutionary (CAME) model is proposed. Each individual in the population act, as an autonomous agent with the goal of optimizing its fitness being able to communicate and select a mate for recombination. Different strategies for recombination correspond to different societies of agents (subpopulations). The asynchronous search process is facilitated by a gradual propagation of the fittest individuals' genetic material into the population. Furthermore, two heuristics are proposed for avoiding local optima and for maintaining population diversity. These are the dynamic dominance heuristic and the shaking mechanism, both being integrated in the CAME model. Numerical results indicate a good performance of the proposed evolutionary asynchronous search model. Particularly, proposed CAME technique obtains excellent results for difficult highly multimodal optimization problems indicating a huge potential for dynamic and multicriteria optimization.