协同进化算法研究进展

为了应对复杂优化问题的高维、大规模、多类变量混合、强约束、多极小、多目标、动态与不确定等诸多求解难点，协同进化已成为改善进化算法性能的有效途径。对此，分别从种群协同、个体协同、算法协同、操作协同、参数协同、策略协同、人机协同等方面，对协同进化算法的代表性研究进展给予了综述，重点总结了协同进化的机制和算法设计，并介绍了协同进化算法在若干领域的应用，最后指出了有待于进一步研究的若干方向和内容。

Advances in co-evolutionary algorithms

To tackle the multiple difficulties of complex optimization problems in terms of high dimension, large scale, mixed-type variables, strong constraints, multiple minima, multiple objectives, dynamic and stochastic environments, etc, co-evolution is an effective way to improve the performances of evolutionary algorithms. An overview on co-evolutionary algorithms is presented in terms of population-collaboration, individual-collaboration, algorithm-collaboration, operatorcollaboration, parameter-collaboration, strategy-collaboration, and human-machine-collaboration. The mechanisms and designs of co-evolutionary algorithms are summarized. The applications of co-evolutionary algorithms in various fields are introduced. Finally, some future research direction and contents are pointed out.