A new hybrid heuristic approach for solving large traveling salesman problem*1

Tsai, Cheng-Fa; Tsai, Chun-Wei; Tseng, Ching-Chang

doi:10.1016/j.ins.2003.11.008

Cited by 115 publications

(52 citation statements)

References 16 publications

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“…Optimal-solution searching by HCA was compared with those of other well-known algorithms, namely, an ACO algorithm combined with fast opposite gradient search (FOGS-ACO) [39], a genetic simulated annealing ant colony system with PSO (GSAACS-PSO) [40], an improved discrete bat algorithm (IBA) [27], set-based PSO (S-CLPSO) [41], a modified discrete PSO with a newly introduced mutation factor C3 (C3D-PSO); results taken from [23], an adaptive simulated annealing algorithm with greedy search (ASA-GS) [11], the firefly algorithm (FA) [42], a hybrid ACO enhanced with dual NN (ACOMAC-DNN) [43], a discrete PSO (DPSO) [26], a self-organizing neural network using the immune system (ABNET-TSP) [44], and an improved discrete cuckoo search algorithm (IDCS) [45]. Table 10 summarizes the comparison results.…”

Section: Benchmark Tsp Instancesmentioning

confidence: 99%

Solving the Traveling Salesman Problem Using Hydrological Cycle Algorithm

Wedyan¹,

Whalley²,

Narayanan³

2018

AJOR

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In this paper, a recently developed nature-inspired optimization algorithm called the hydrological cycle algorithm (HCA) is evaluated on the traveling salesman problem (TSP). The HCA is based on the continuous movement of water drops in the natural hydrological cycle. The HCA performance is tested on various geometric structures and standard benchmarks instances. The HCA has successfully solved TSPs and obtained the optimal solution for 20 of 24 benchmarked instances, and near-optimal for the rest. The obtained results illustrate the efficiency of using HCA for solving discrete domain optimization problems. The solution quality and number of iterations were compared with those of other metaheuristic algorithms. The comparisons demonstrate the effectiveness of the HCA.

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Section: Benchmark Tsp Instancesmentioning

confidence: 99%

Solving the Traveling Salesman Problem Using Hydrological Cycle Algorithm

Wedyan¹,

Whalley²,

Narayanan³

2018

AJOR

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“…We had to dismiss all approaches using n exploring ants where n is equal to the number of cities because of their inapplicable memory consumption and runtimes in huge graphs. We selected ACS+NN algorithm proposed in [25] considering its improvements and applicability in huge search spaces. In this algorithm the nearest neighbor (NN) strategy is used as a kind of local search method to boost the efficiency of ACS by selecting nearest neighbors in craeting the initializing tour.…”

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy

Lalbakhsh

Zaeri

Lalbakhsh

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThe paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics.

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“…Xiong Weiqing, Yu Shunhao, Zhao Jieyu proved that the mutation operator in ant algorithm can significantly improve the results [23]. Cheng -Fa Tsai, Chun -Wei Tsai, and Ching -Chang Tseng solve the TSP problem with dividing the paths into several, so all the ants can exchange between populations of pheromone [24]. However, the above literature is a strategy or rule to make improvements on the ant colony algorithm, and does not take into account the relationship between the policies or rules.…”

Section: Introductionmentioning

confidence: 99%