基于信息交流策略的连续域蚁群优化算法

连续域蚁群优化算法（ACOR）在求解优化问题时，全局寻优能力弱，寻优结果精度低。受自然界中优秀的个体之间相互交流和结合可以产生较优的后代的启发，提出了一种基于信息交流策略的连续域蚁群优化算法（ICACO）。ICACO算法在对解的更新过程中选取一部分较优解利用信息交流策略进行处理得到候选解，并采用贪婪方式接受能够改善解的质量的候选解。通过标准测试函数对所提算法进行测试，实验结果表明ICACO算法能够有效地提高ACOR算法寻优结果的精度并加快收敛速度。该算法与相关改进的连续域蚁群算法及其他智能优化算法相比全局搜索能力更高，效果更好。

Continuous Domain Ant Colony Optimization Algorithm Based on Information Exchange Strategy

When the continuous domain ant colony optimization algorithm（ACOR） solves the optimization problem, the global optimization ability is weak, and the accuracy of the optimization result is low. Inspired by the mutual exchange and combination between the excellent individuals in nature can produce better offspring, a continuous domain ant colony optimization algorithm（ICACO） based on information exchange strategy is proposed. The ICACO algorithm selects some of the better solutions in the process of updating the solution and uses the information exchange strategy to process the candidate solutions, and adopts a greedy method to accept candidate solutions that can improve the solution quality. The proposed algorithm is tested by the standard test function. The experimental results show that the ICACO algorithm can effectively improve the accuracy of the ACOR algorithm and speed up the convergence. This algorithm has better global search ability and better performance than related improved continuous domain ant colony algorithm and other intelligent optimization algorithms.