A multi-agent complex network algorithm for multi-objective optimization

To deal with the multi-objective optimization problems (MOPs), this study proposes a new Multi-Objective Multi-Agent Complex Network Optimization Algorithm called MOMCNA based on the idea of Cellular genetic algorithm (CGA) and the Multi-agent complex network. Compared with the traditional CGA for multi objective problem, the individuals in the population of MOMCNA have more features of intelligent agent, the new form of neighborhood for the population, private archive for individuals, the new strategy of “local-global” genetic operator and the chaotic mutation are proposed in the new algorithm to balance the convergence and diversity of the algorithm. Seventeen unconstrained multi-objective optimization problems and seven many-objective problems are introduced and tested to evaluate the new algorithm, in addition, the classical traffic assignment problem based on different system optimum principle is also established to evaluate the new algorithm. The comparison between MOMCNA and other classical algorithms shows that the proposed MOMCNA proves to be competitive in dealing with multi-objective and many-objective optimization problems and the structure of the complex network made up of population also has effect on algorithm’s performance.