基于中心解的改进人工蜂群算法

为了解决人工蜂群(ABC)算法在用于函数优化时所具有的局部探索能力不强、收敛精度不高的问题,提出一种基于中心解的人工蜂群算法。该算法结合中心解和当前最优候选解的优点,并将中心解引入到跟随蜂的局部变异策略中。跟随蜂采用轮盘赌的形式,选择某些适应度值较好的蜜源,在雇佣蜂中心解的基础上深度局部寻优,并在每次迭代中逐维更新蜜源每一维度的值。为了验证该算法的有效性,选择六个基准测试函数对三种算法进行仿真对比实验。与标准ABC算法和Best-so-far ABC算法相比,改进的ABC算法的求解精度有较大幅度提高,特别是对于Rastrigin函数,两种不同维数下均达到了理论最优值。实验结果表明:所提算法在收敛速度和寻优精度上都有明显改善。

Improved artificial bee colony algorithm based on central solution

An improved Artificial Bee Colony (ABC) algorithm for function optimization based on central solution was proposed to solve the problem of poor local searching capacity and low accuracy of conventional ABC algorithm. The algorithm combined the advantage of the central solution, which was introduced into the local search process of onlooker bees. Onlooker bees chose some nectar sources with better fitness values using roulette, did the further local optimization based on central solution and updated the value of each dimension of nectar source in every iteration. In order to verify the validity of the proposed algorithm, six standard functions were selected to simulate and compare with the other tow algorithms including ABC algorithm and Best-so-far ABC algorithm, the proposed algorithm greatly improved the quality of solution and reached theoretical optimal value especially for Rastrigin function. The results show that the proposed algorithm has significant improvement on solution accuracy and convergence rate.