Applications of artificial atom algorithm to small-scale traveling salesman problems

Yildirim, Ayse; Karcı, Ali

doi:10.1007/s00500-017-2735-z

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…Traveling salesmen problem(TSP) [69,70] is one which has commanded much attention of mathematicians and computer scientists specifically, because it is so easy to describe and so difficult to solve. This problem can simply be stated as follow: a search for the shortest closed tour that visits each city once and only once.…”

Section: Application Of the Icmpaco Algorithm For Solving Tsp A Tmentioning

confidence: 99%

An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

2019

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In this paper, an improved ant colony optimization(ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability. INDEX TERMS Co-evolution mechanism, ACO, pheromone updating strategy, pheromone diffusion mechanism, hybrid strategy, assignment problem.

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Section: Application Of the Icmpaco Algorithm For Solving Tsp A Tmentioning

confidence: 99%

An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

2019

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“…It should be noted that, in Table 2, algorithms 1 to 17 represent the new algorithm in this paper, HMMAS [13], HEAS [16], ACO-3Opt [17], DABC-NO [20], DCSA [22], RKCS [23], ABO [24], DBA [25], FFOA [26], GA-MRLH [27], AAA [28], ICA [30], BHA [31], SA-SOSOA [32], DSOSA [33], and DABC-TAC [34], respectively.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…Because the TSP is a well-known NP-hard combinatorial optimization problem that is computationally difficult, in addition to ACO, many other new metaheuristic optimization algorithms have been applied to solve it, such as the quantum heuristic algorithm (QHA) [19], the discrete artificial bee colony algorithm with a neighborhood operator (DABC-NO) [20], the shrinking blob algorithm (SBA) [21], the discrete cuckoo search algorithm (DCSA) [22], the random-key cuckoo search (RKCS) [23], the African buffalo optimization (ABO) [24], the discrete bat algorithm (DBA) [25], the fruit fly optimization algorithm (FFOA) [26], a hybrid algorithm using a GA and a multiagent reinforcement learning heuristic (GA-MRLH) [27], the artificial atom algorithm (AAA) [28], the greedy flower pollination algorithm (GFPA) [29], the imperial competitive algorithm (ICA) [30], the black hole algorithm (BHA) [31], the simulated annealing-based symbiotic organisms search optimization algorithm (SA-SOSOA) [32], the discrete symbiotic organisms search algorithm (DSOSA) [33], the hybrid discrete artificial bee colony algorithm with a threshold acceptance criterion (DABC-TAC) [34], a minimum spanning tree-based heuristic (MSTH) [35], a genetic algorithm with local operators (GAL) [36], a new hybrid optimization algorithm based on wolf pack search and local search (WPS-LS) [37], discrete spider monkey optimization (DSMP) [38], discrete pigeon-inspired optimization (DPIO) [39], and the parthenogenetic algorithm (PGA) [40], and so on. For those algorithms, many are newly proposed metaheuristic algorithms, such as QHA, SBA, DCSA, RKCS, ABO, DBA, FFOA, AAA, GFPA, ICA, BHA, DSOSA, MSTH, DSMP, DPIO, and PGA.…”

Section: Introductionmentioning

confidence: 99%

New Ant Colony Optimization Algorithm for the Traveling Salesman Problem

Gao¹

2020

IJCIS

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As one suitable optimization method implementing computational intelligence, ant colony optimization (ACO) can be used to solve the traveling salesman problem (TSP). However, traditional ACO has many shortcomings, including slow convergence and low efficiency. By enlarging the ants' search space and diversifying the potential solutions, a new ACO algorithm is proposed. In this new algorithm, to diversify the solution space, a strategy of combining pairs of searching ants is used. Additionally, to reduce the influence of having a limited number of meeting ants, a threshold constant is introduced. Based on applying the algorithm to 20 typical TSPs, the performance of the new algorithm is verified to be good. Moreover, by comparison with 16 state-of-the-art algorithms, the results show that the proposed new algorithm is a highly suitable method to solve the TSP, and its performance is better than those of most algorithms. Finally, by solving eight TSPs, the good performance of the new algorithm has been analyzed more comprehensively by comparison with that of the typical traditional ACO. The results show that the new algorithm can attain a better solution with higher accuracy and less effort.

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“…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...…”

Section: Traveling Salesman Problem and Its Related Workmentioning

confidence: 99%

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

Gao

2020

Soft Comput

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To improve ant colony optimization (ACO) for traveling salesman problem (TSP), its two main strategies which are tour construction and pheromone updating have been modified, and one modified ACO (MACO) has been proposed. For the first strategy (tour construction strategy), one new method to construct tours by combining paths of two meeting ants has been applied. And for second strategy (pheromone updating strategy), one new method to polarize pheromone density of all paths has been proposed. Based on the applications for 40 standard benchmark TSP instances (datasets) ranging from 29 to 13,509 cities, the good performance of the MACO is verified. To verify the MACO deeply, based on the applications for some standard TSP instances, the computing results of MACO are compared with three typical state-of-the-art algorithms based on ACO methods. Moreover, based on the applications for some standard TSP instances, the computing results of MACO are compared with 10 state-of-the-art metaheuristic algorithms. The comparison studies show that the MACO can attain the optimal solution with higher accuracy, no matter how complicated the TSPs are. And its performance is the best, comparing to all state-of-the-art algorithms. At last, two new strategies used in MACO have been analyzed comprehensively by the applications for 25 TSPs. The results show that the first strategy is mainly used to improve the computing efficiency, and the second one is mainly used to improve the computing effect.

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Applications of artificial atom algorithm to small-scale traveling salesman problems

Cited by 15 publications

References 19 publications

An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

New Ant Colony Optimization Algorithm for the Traveling Salesman Problem

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

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