We propose a new method based on discrete Artificial Bee Colony algorithm (DABC)
IntroductionTraveling salesman problem (it is abbreviated in TSP) [1] is an important combinational optimization problem in the area of mathematics. It belongs to Non-Deterministic Polynomial (NP) problem [2]. Although there are some precise algorithms which can be used to solve the problem, the principle of precise algorithms is complex, and it can produce "combination explosion problem" along with the increase number of city, therefore, the domestic and foreign scholars have been trying to seek a highly efficient and stable algorithm for solving this complex problem.Artificial bees colony algorithm (ABC) [3] is one of heuristic search algorithms based on swarm intelligence, which was proposed by Karaboga et al. [4] in 2005. ABC algorithm [5] is based on the self-organization of the swarm simulation model with the advantages of less setting parameters, strong robustness, it has received extensive attention of scholars both at home and abroad, and been applied in many fields.Nowadays, many researchers have studied the new algorithm about Artificial bees colony for TSP. Sharma P et al. [6] showed that bees speculative modified over time and based on the best solution found by the bee itself and the swarms of bees were dynamically divided into smaller groups and search process was performed by an independent smaller group of bees. Hong-Tao et al. [7] proposed a discrete artificial bee colony algorithm. And he introduced a tabu list and a repulsion operator.The above methods almost are used for solving continuous domain optimization problems. However, ABC algorithm is relatively few used in the aspect of discrete domain application. To improve the performance of TSP solution, we propose improved discrete Artificial Bee Colony algorithm through redefining leading bees, following bees and scouts which better coordinates and balances the exploration and mining process of ABC algorithm. To facilitate the description, this paper also gives some definitions. We present a new discrete Artificial Bee Colony algorithm for traveling salesman problem. This new scheme takes balance of space exploration and the local refinement into consideration. Finally, we conduct some experiments to verify its performance. The results show that the new algorithm has a good effect on solving TSP question.
As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.