A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem

Gülcü, źAban; Mahi, Mostafa; Baykan, Ömer Kaan; Kodaz, Halife

doi:10.1007/s00500-016-2432-3

Cited by 135 publications

(82 citation statements)

References 34 publications

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“…As we can see, at Phase 3, there is an improvement in the quality of the solutions delivered by Phase 2. Table 8 shows the summary of the comparison statistics of the solutions delivered by our algorithm CII with the solutions obtained by the heuristics that we have mentioned in the introduction (namely, DFACO [10], ACO-3Opt [10], ESACO [7], PACO-3Opt [8], DPIO [12], ACO-RPMM [9], Partial ACO [6], and PRNN [11]). We may observe in Table 9 that algorithm CII has attained an improved approximation for 17 instances.…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…Effective Strategies + ACO (ESACO) uses pheromone values directly in the 2-opt local search for the solution improvement and reduces the pheromone matrix, yielding linear space complexity (see, for example, Ismkman [7]). Parallel Cooperative Hybrid Algorithm ACO (PACO-3Opt) uses a multi-colony of ants to prevent a possible stagnation (see, for example, Gülcü et al [8]). In a very recent Restricted Pheromone Matrix Method (RPMM) [9], the pheromone matrix is reduced with a linear memory complexity, resulting in an essentially lower memory consumption.…”

Section: Introductionmentioning

confidence: 99%

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Simple Constructive, Insertion, and Improvement Heuristics Based on the Girding Polygon for the Euclidean Traveling Salesman Problem

et al. 2019

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The Traveling Salesman Problem (TSP) aims at finding the shortest trip for a salesman, who has to visit each of the locations from a given set exactly once, starting and ending at the same location. Here, we consider the Euclidean version of the problem, in which the locations are points in the two-dimensional Euclidean space and the distances are correspondingly Euclidean distances. We propose simple, fast, and easily implementable heuristics that work well, in practice, for large real-life problem instances. The algorithm works on three phases, the constructive, the insertion, and the improvement phases. The first two phases run in time O(n 2 ) and the number of repetitions in the improvement phase, in practice, is bounded by a small constant. We have tested the practical behavior of our heuristics on the available benchmark problem instances. The approximation provided by our algorithm for the tested benchmark problem instances did not beat best known results. At the same time, comparing the CPU time used by our algorithm with that of the earlier known ones, in about 92% of the cases our algorithm has required less computational time. Our algorithm is also memory efficient: for the largest tested problem instance with 744,710 cities, it has used about 50 MiB, whereas the average memory usage for the remained 217 instances was 1.6 MiB.

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Section: Implementation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simple Constructive, Insertion, and Improvement Heuristics Based on the Girding Polygon for the Euclidean Traveling Salesman Problem

et al. 2019

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“…Saban g ü LC ü [17] et al proposed a parallel collaborative hybrid algorithm PACO-3 opt. Based on the master-slave model, the algorithm divides the entire ant colony into several sub-ant colonies.…”

Section: Related Workmentioning

confidence: 99%

Dynamic ant colony algorithm based on cw saving Value

Yao

Gao

2018

MATEC Web Conf.

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As a swarm intelligence optimization algorithm, ant colony algorithm (ACO) has a good application in combinatorial optimization problems, in which traveling salesman problem (TSP) is an important application of ACO algorithm. It shows the powerful ability of ant colony algorithm to find short paths through graphics. However, there are obvious defects in the ant colony algorithm. When the scale of the ant colony is large, the convergence time of the algorithm becomes longer and the local optimal state is easy to fall into. In this paper, a dynamic pheromone ant colony optimization algorithm based on CW saving algorithm is proposed. Initially, a general path range is found by CW saving value algorithm, and the pheromone matrix can be reasonably configured, so that the ant colony algorithm can quickly get a better solution in the initial optimization. At the same time, the optimization scheme can be adjusted in real time according to the situation of path optimization. Large ant colony searches for other paths. Combined with 3-opt local search algorithm, the ant colony can find the optimal path more quickly. The experimental results show that the improved ant colony algorithm has better convergence speed and solution quality than other ant colony algorithms.

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“…[15]. Parallel [16] and hybrid methods [17] have been also proposed in recent years. In the literature, there are many studies conducted on TSP in detail.…”

Section: Introductionmentioning

confidence: 99%

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Gulcu¹,

Kahramanlı²

2019

ijisae

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Nowadays, the systems that are inspired by biological structures have gained importance and attracted the attention of researchers. The Multiple Travelling Salesman Problem (MTSP) is an extended version of the TSP. The aim in the MTSP is to find the tours for m salesmen, who all start and end at the depot, such that each intermediate node is visited exactly once and the total cost of visiting nodes is minimized. The Particle Swarm Optimization (PSO) algorithm which is a meta-heuristic algorithm based on the social behaviour of birds. In this article, 2 algorithms based on PSO, called APSO and HAPSO, were proposed to solve the MTSP. The APSO algorithm is based on the PSO and 2-opt algorithms, the path-relink and swap operators. While the HAPSO algorithm is based on the GRASP, PSO and 2-opt algorithms, the path-relink and swap operators. In the experiments, 5 TSP instances are used and the algorithms are compared with the GA and ACO algorithms. According to the results, the HAPSO algorithm has the better performance than the other algorithms on the most instances. Moreover the HAPSO algorithm produces more stable results than the APSO algorithm and the performance of the HAPSO algorithm is better in all the MTSP instances. Therefore, the HAPSO algorithm is more robust than the APSO algorithm.

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A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem

Cited by 135 publications

References 34 publications

Simple Constructive, Insertion, and Improvement Heuristics Based on the Girding Polygon for the Euclidean Traveling Salesman Problem

Simple Constructive, Insertion, and Improvement Heuristics Based on the Girding Polygon for the Euclidean Traveling Salesman Problem

Dynamic ant colony algorithm based on cw saving Value

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

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