Combinatorial Optimization of Multi-agent Differential Evolution Algorithm

Combinatorial optimization is often with the local extreme point in large numbers. It is usually discontinuous, multidimensional, non-differentiable, constraint conditions, highly nonlinear NP problem. In this paper, according to the characteristics of combinatorial optimization problem, we put forward the combination optimization of multi-agent differential evolution algorithm (COMADE) through combining the multi-agent and differential evolution algorithm, in which we designed the competition behavior and self-learning behavior of agent. Through performance testing of strong connected, weak connected and overlap connected deceptive function on the COMADE algorithm, the results show that the COMADE algorithm is effective and practical value.