Chainlike multi-population multi-agent evolutionary algorithm
链式多种群多智能体进化算法

We propose a novel chainlike multi-population multi-agent evolutionary algorithm which combines the dynamic neighborhood environment chainlike structure with the evolutionary framework of multi-population. This algorithm provides the evolution structure for multi-populations interaction. Agents in the population increase their own energy by competition, cooperation and self-study with its dynamic neighborhood agents. The chainlike structure improves the efficiency of algorithms and reduces the computational complexity. The interaction of information among various populations in a regular period of time improves the diversity of the population and decreases the possibility of sticking at local optima. Theoretical analysis and simulation of multiple test functions show that the new algorithm is very good for handling high-dimension optimization problems.