自适应混合文化蜂群算法求解连续空间优化问题

提出一种自适应混合文化蜂群算法求解连续空间优化问题。算法中群体空间采用最优觅食理论改进群体更新方式；信念空间通过云模型算法和最优排序差分变异策略对知识进行更新；利用混沌算法和反向学习算法进化外部空间；3种空间通过自适应的影响操作来实现知识的交换。典型复杂函数测试表明，该算法具有很好的收敛精度和计算速度，特别适宜于多峰值函数寻优。

Adaptive Mixed Culture Artificial Bee Colony Algorithm for Continuous Space Optimization Problems

An adaptive mixed culture artificial bee colony algorithm (AMC-ABC) is proposed to solve continuous space optimization problem. In the algorithm, community space is evolved by the improved group update way with optimal foraging theory; the knowledge of belief space is updated by the cloud model algorithm and optimal sorting differential mutation strategy; the outer space is evolved by chaos algorithm and opposition-based learning algorithm; and the knowledge exchange of three kinds of spatial is realized by adaptive acceptance operation and effect of operation. Simulation results of the typical complex functions show that the algorithm has fine the convergence precision and computing speed, particularly suitable for optimization the multimodal function.