A pseudo-dynamic search ant colony optimization algorithm with improved negative feedback mechanism |
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| Affiliation: | 1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei 430065, China;2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, Hubei 430065, China;3. College of Computer and Communication, Hunan University of Technology, Hunan 412000, China;1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;3. College of Computer and Information Sciences, Chongqing Normal University, Chongqing 401331, China;4. BaoAn Central Hospital of Shenzhen, Shenzhen, 518101, China;1. Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, ShangHai 201804, PR China;2. School of Informatics and Engineering, Suzhou University, Suzhou 234000, PR China |
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| Abstract: | To solve low convergence precision and slow convergence speed, a pseudo-dynamic search ant colony optimization algorithm with improved negative feedback mechanism (PACON) is proposed. Firstly, the algorithm introduces an angle in the pheromone transfer rule. Through the rule for calculating the angle, multiple cities with smaller angles are also included in the next candidate city list. It affects the probability of city selection and enhances the algorithm’ performance to avoid local optimization. Secondly, the algorithm updates the pheromone concentrations on the worst and optimal path simultaneously, and enhances the weights of the pheromone concentrations on the optimal path. It improves the convergence speed of the algorithm. Based on experiments adopting TSPLIB data sets, the results demonstrate the improved algorithm improves the convergence accuracy by at least 1.26% and increases the convergence speed by at least 9.5%, both on large-scale and small-scale urban data. The novel algorithm will improve convergence precision and speed better. |
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| Keywords: | Ant colony algorithm Pseudo-dynamic search Angle Negative feedback |
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