基于改进搜索策略和混沌机制的人工蜂群算法

人工蜂群算法具有较强的探索能力，但是开采能力差、搜索精度低、后期收敛速度慢。针对以上问题，本文提出一种基于混沌机制的人工蜂群算法，在搜索方程中引入历史平均最优解，避免探索和开采能力的失衡；迭代后期，若种群陷入局部极值，采用混沌序列对种群进行变异，以增强算法的开采能力和求解的质量，保持种群的多样性。经过函数测试结果表明，改进后的算法在求解速度和精度上均优于基本ABC算法和其他改进算法。 

Artificial Bee Colony Algorithm Based on Improved Search Strategy and Chaotic Mechanism

Artificial colony algorithm has strong ability of exploration, but has poor exploitation ability, low search accuracy, slow convergence speed during the later period. In order to solve the above problems, an artificial colony algorithm based chaotic mechanism is proposed. In order to avoid the imbalance between the exploration and exploitation ability, the historical average optimal solution was added to the search strategy; and chaotic sequence was used in the late period to make the population mutation if the population falls into local extremum, to enhance the exploitation ability and the quality of solutions, maintain the diversity of the population. Experimental results tested on functions show that the improved algorithm is superior to the basic ABC algorithm and other improved algorithms in solving speed and precision. 