A New Method Used for Traveling salesman problem Based on Discrete Artificial Bee Colony Algorithm

Abstract

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.

Modified ant colony optimization with improved tour construction and pheromone updating strategies for traveling salesman problem

“…These methods include greedy algorithm (Hoos and Stützle 2005), stimulated annealing (Meer 2007;Zhan et al 2016), tabu search (Fiechter 1994;Misevičius et al 2005), neural network (Créput and Koukam 2009;Mulder and Wunch 2003), genetic algorithm (Cunkas and Ozsaglam 2009;Khan et al 2009;Tsai et al 2014), particle swarm optimization (Cunkas and Ozsaglam 2009;Shi et al 2007), ACO (Dorigo and Gambardella 1997;Guo and Liu 2011;Mei et al 2009), etc. Currently, many other new metaheuristic optimization algorithms have been applied to solve it too, such as quantum heuristic algorithm (Bang et al 2012), artificial bee colony algorithm (Kıran et al 2013;Meng et al 2016;Wong et al 2008), shrinking blob algorithm (Jones and Adamatzky 2014), discrete cuckoo search algorithm (Ouaarab et al 2014), African buffalo optimization (Odili and Kahar 2016), discrete bat algorithm (Saji and Riffi 2016), fruit fly optimization algorithm (Huang et al 2017), artificial atom algorithm (Yildirim and Karci 2018), black hole algorithm (Hatamlou 2018), symbiotic organisms search algorithm , and a minimum spanning tree-based heuristic (Kumar et al 2018). Moreover, some hybrid heuristic algorithms have also been proposed to solve TSP, such as cooperative genetic ant system (Dong et al 2012), hybrid max-min ant system integrated with a four vertices and three lines inequality (Wang 2015), hybrid elitist-ant system (Jaradat 2018), hybrid method based on ACO and 3-Opt algorithm (Gülcü et al 2018), hybrid method based on ACO and artificial bee...…”

An Improved Artificial Bee Colony Algorithm for Staged Search

“…Because the existing ABC algorithms [11][12][13] have the defect of restricting the escape of precocious individual [13]. We deign a new escape scouter strategy.…”

Motorcycle-Security using Position Searching Algorithm Based on Hybrid Fuzzy-Dijkstra

“…Therefore, we compare TVAC with our new method (EM-CMLSQN), and the simulation results show that EM-CMLSQN has a better convergence rate and performance of jumping out of local solution than PSO and TVAC method. We also apply EMCMLSQN algorithm into path planning problem, and the results represent that EM-CMLSQN algorithm can search the optimal path more precisely and can be better applied into solving discrete domain problems than genetic algorithm and PSO algorithm [10,11]. The basic EM algorithm is composed of initialization, local search, resultant force calculation, particle displacement and judgment terminated.…”