基于近邻牵引算子的离散黑猩猩优化算法

针对旅行商问题的特点,提出基于近邻牵引算子的离散黑猩猩优化算法.首先,引入优质片段的概念,并结合每个群组的最优个体设计其检索方法,以提高组内学习策略的效果,根据组合优化问题特点对黑猩猩群体的狩猎过程进行离散化表示;其次,通过组间交流机制消除部分个体路径交叉;最后,为了克服传统的邻域搜索算子收敛慢和搜索效率低的缺点,提出一种新的邻域搜索方式—–近邻牵引算子,其搜索目的更加明确、收敛更高效,并设计自适应概率扰动调控策略,以有效平衡算法的探索与开发.对30个TSP标准数据集进行实验,结果表明,所设计的离散黑猩猩优化算法求解质量高、收敛速度快,可以应用于组合优化问题求解.

Discrete chimp optimization algorithm based on neighbour traction operator

According to the characteristics of the traveling salesman problem, a discrete chimp optimization algorithm based on a nearest neighbour traction operator is proposed. Firstly, we introduce the concept of high-quality fragment, and design a retrieval method combining with the optimal individuals of each group to improve the efficiency of the learning strategy with intra-group, and discretize the hunting process of chimpanzee groups in combination with the characteristics of combinatorial optimization problems. Then, through an inter-group communication mechanism to eliminate path crossing of some individuals. Finally, in order to overcome the shortcomings of slow convergence and low search efficiency of the traditional neighborhood search operators, a new neighborhood search method, the neighbour traction operator, is proposed, which has clearer search purpose and more efficient convergence, and an adaptive probabilistic disturbance control strategy is designed to effectively balance the exploitation and exploration of the algorithm. Experiments are conducted on 30 instances from the TSP standard datasets. The experimental results show that the designed discrete chimp optimization algorithm has high solution quality and fast convergence speed, and can be applied to the solution of combinatorial optimization problems.