Ant Colony Optimization algorithms have been successfully applied to solve the Traveling Salesman Problem (TSP). However, they still have a tendency to fall into local optima, mainly resulting from poor diversity, especially in those TSPs with a great deal of cities. To address this problem, and further obtain a better result in big-scale TSPs, we propose an algorithm called Multiple Colonies Ant Colony Optimization Based on Pearson Correlation Coefficient (PCCACO). To improve the diversity, firstly, we introduce a novel single colony termed Unit Distance-Pheromone Operator which along with two other classic ant populations: Ant Colony System and Max-Min Ant System, make the final whole algorithm. A Pearson correlation coefficient is further employed to erect multi-colony communication with an adaptive frequency. Besides that, an initialization is applied when the algorithm stagnant, which helps it to jump out of the local optima. Finally, we render a dropout approach to reduce running time. Extensive simulations in TSP demonstrate that our algorithm can get a better solution with a reasonable variation.

Aiming at the problems of poor diversity and slow convergence of ant colony algorithm, dynamic multi-role adaptive collaborative ant colony optimization (MRCACO) is proposed in this paper, and it applies to robot path planning. Firstly, an adaptive dynamic complementary algorithm is proposed to form a heterogeneous multi-colony together with ACS and MMAS, which complement each other in performance. Secondly, a multi-role adaptive cooperation mechanism is proposed to realize the exchange and sharing of information. The mechanism includes two strategies: one is an elite attribute learning strategy, which highlights the role of elite attribute and improves the comprehensive performance of ACS and MMAS; The second is the pheromone balancing strategy, which is executed when the algorithm is stagnant to make the algorithm jump out of the local optimal. Further, the effectiveness and superiority in the algorithm are demonstrated by the experimental analysis of multiple TSP instances. Finally, the algorithm presented in this paper is applied to the path planning of the robot, two different deadlock rollback strategies are proposed to solve the deadlock problem and improve the efficiency of the algorithm. The results of a practical application show that the algorithm is feasible to solve the path planning problem.

Ant Colony Optimization has achieved good results in solving Traveling Salesman Problem (TSP), it has a tendency to fall into local optima and the convergence speed is limited. To address this problem, multi-colony ant colony optimization based on the generalized Jaccard similarity recommendation strategy (JCACO) is proposed. Firstly, two classical ant populations, Ant Colony System and Max-Min Ant System are selected to form heterogeneous multi-colony. Secondly, attribute-based collaborative filtering recommendation mechanism is proposed to balance the diversity and convergence of the algorithm, three strategies have been implemented under this recommendation mechanism: The attribute cross-learning strategy is used to highlight the effect of excellent attributes and improve the attribute comprehensive performance; According to the diversity results of the population measured by information entropy, the attribute recommendation learning strategy is used to enrich the diversity of the population adaptively; The pheromone reward strategy is implemented on the public path to accelerate the convergence speed; Among which, according to the generalized Jaccard similarity coefficient, the most suitable communication object is recommended in order to achieve the best learning efficiency. Finally, when the algorithm stagnates, the elite reverse learning mechanism is used to jump out of the local optimum. Experimental results show that JCACO has good performance and high stability in TSP instances, especially in large-scale TSP instances.

To balance the convergence speed and the solution's diversity in the large-scale travel salesman problem (TSP), this paper proposes a new heuristic communication heterogeneous dual population ant colony optimization (HHACO). First, the main characteristics of HHACO are the heuristic communication and the two heterogeneous ant colonies. Heuristic communication, an indirect communication strategy, helps improve the deviation of solution. Heterogeneous ant colonies are beneficial to balance the convergence speed and the diversity of solution, in which one ant colony is in charge of solution's diversity and the another one in charge of convergence speed inspiring from nature evolution with self-adaptive ability. Besides, this paper takes advantage of orthogonal test to discuss the parameters in HHACO algorithm and a better parameters' set is obtained. Then, HHACO algorithm is applied to solve TSP, and meanwhile characteristics of different ant colonies in HHACO are discussed. Finally, HHACO is compared with the other dual colonies algorithms and several classic ant colony optimization algorithms, and results suggest that the HHACO has a better performance in the large-scale problem.