求解大规模问题协同进化动态粒子群优化算法

随着工程技术的发展与优化问题数学模型的完善，许多优化问题从低维优化发展成高维的大规模复杂优化，成为实值优化领域的一个热点问题.通过对大规模问题的特点分析，提出了随机动态的协同进化策略，将其加入动态多种群粒子群优化算法中，实现了对种群粒子和决策变量的双重分组.最后使用CEC2013的大规模全局优化算法的测试集对新算法进行测试，通过和其它算法的对比，验证算法的有效性.

Dynamic Multi-Swarm Particle Swarm Optimization with Cooperative Coevolution for Large Scale Global Optimization

With the development of engineering technology and the improvement of mathematical model, a large number of optimization problems were developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic dynamic cooperative coevolution strategy was proposed. The strategy was added to the dynamic multi-swarm particle swarm optimization algorithm. And the dual grouping of population and decision variables was realized. Next, the performance of the novel optimization on the set of benchmark functions provided for the CEC2013 Special Session on Large Scale optimization is reported. Finally the validity of the algorithm was verified by comparing with other algorithms.