Discrete Grey Wolf Optimizer for symmetric travelling salesman problem

Panwar, Karuna; Deep, Kusum

doi:10.1016/j.asoc.2021.107298

Cited by 85 publications

(38 citation statements)

References 34 publications

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“…Some other recent bio-inspired algorithms for TSP are Velocity Tentative PSO (VTPSO) [8], Discrete Cat Swarm Optimization (DCSO) [36], Discrete Grey Wolf Optimizer (DGWO) [37], Discrete Cuckoo Search (DCS) algorithm [9], ABC algorithm with variable degree of perturbation [38], and Whale Optimization Algorithm (WOA) [39].…”

Section: Solving Tsp With Other Prominent Bio-inspired Methodsmentioning

confidence: 99%

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

2022

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Ant Colony Optimization (ACO) is a practical and well-studied bio-inspired algorithm to generate feasible solutions for combinatorial optimization problems such as the Traveling Salesman Problem (TSP). ACO is inspired by the foraging behavior of ants, where an ant selects the next city to visit according to the pheromone on the trail and the visibility heuristic (inverse of distance). ACO assigns higher heuristic desirability to the nearest city without considering the issue of returning to the initial city or starting point once all the cities are visited. This study proposes an improved ACO-based method, called ACO with Adaptive Visibility (ACOAV), which intelligently adopts a generalized formula of the visibility heuristic associated with the final destination city. ACOAV uses a new distance metric that includes proximity and eventual destination to select the next city. Including the destination in the metric reduces the tour cost because such adaptation helps to avoid using longer links while returning to the starting city. In addition, partial updates of individual solutions and 3-Opt local search operations are incorporated in the proposed ACOAV. ACOAV is evaluated on a suite of 35 benchmark TSP instances and rigorously compared with ACO. ACOAV generates better solutions for TSPs than ACO, while taking less computational time; such twofold achievements indicate the proficiency of the individual adoption techniques in ACOAV, especially in AV and partial solution update. The performance of ACOAV is also compared with the other ten state-of-the-art bio-inspired methods, including several ACO-based methods. From these evaluations, ACOAV is found as the best one for 29 TSP instances out of 35 instances; among those, optimal solutions have been achieved in 22 instances. Moreover, statistical tests comparing the performance revealed the significance of the proposed ACOAV over the considered bio-inspired methods.

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Section: Solving Tsp With Other Prominent Bio-inspired Methodsmentioning

confidence: 99%

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

2022

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“…To verify the performance of the CACO algorithm, 20 independent experiments were conducted in different size city sets and compared with the latest improved particle swarm optimization algorithm MPSO [39] and wolf swarm optimization algorithm D-GWO [40] so far, and the results are shown in Tables 3 and 4.…”

Section: Comparison With the Latest Improved Algorithmmentioning

confidence: 99%

Multiple Ant Colony Algorithm Combining Community Relationship Network

2022

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Ant colony algorithm can better deal with combinatorial optimization problems, but it is still difficult to balance the solution accuracy and convergence speed facing large-scale TSP. Nowadays, most scholars focus on the route information of better ants for improvement, while ignoring the route information of general ants with a large base. So, this study proposes the multiple ant colony algorithm combining community relationship network (CACO) by collecting route information of all ants and constructing a route relationship network to improve the accuracy of the solution. The network is divided into a number of small communities that reflect the affinity of multiple colony ants to different cities through community detection with modularity. Within the communities, CACO use the excellent roue exploration ability of the ant colony algorithm to identify high-quality route segments, integrating the pheromones of high-quality segments in the communities to provide pheromone feedback to the multiple colony ants for better route exploration. The three parts of route information collection, community detection and pheromone feedback form a feedback loop, which keeps cycling when multiple populations ants explore, and each cycle will drive the result closer to the optimal solution. Meanwhile, CACO proposes a mutual assistance strategy to improve the exploration ability of multiple colony ants by complementing each other according to the different states of superior and inferior populations. To test the performance of CACO, 28 TSP instances are compared with the well-known improved algorithms are compared and results show CACO outperforms other improved algorithms significantly, especially in large-scale TSP.

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“…The algorithm which combines these two abilities nicely can refrain itself from converging prematurely in the early stages and quickly converges to the global optimal at the end of optimization. In consequence, meta-heuristic algorithms perform better in TSP with less computation time compared with exact algorithm and heuristic algorithm, it can be summarized into three categories: (1) evolution-based which consists of the genetic algorithm [15], [16]; differential evolution [17]; (2) physics-based which consists of the water cycle algorithm [18], randomized gravitational emulation search algorithm [19]; (3) swarm intelligencebased which consists of ant colony optimization algorithm [20], particle swarm optimization [21], bat algorithm [22], grey wolf optimizer [23], etc.…”

Section: Introductionmentioning

confidence: 99%

“…In the first class, Akhand [26] and Khan [27], [28] employed swap sequence and swap operator to keep the discrete format by swapping the positions of two genes, although the above algorithms do not need to be decoded, they play a minor role in the offspring quality improvement of its heuristic insufficiency and exhibit low convergence speed. The literature [18], [23], [29] employed hamming distance to redesign the individual generation operator according to the characteristics of TSP. Although these methods above can improve the solution quality and have strong search ability, they are prone to stick to local optimum and the algorithm design idea is changed.…”

Section: Introductionmentioning

confidence: 99%

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

et al. 2022

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The traveling salesman problem (TSP) is one of the most extensively studied problems in the combinatorial optimization area and still presents unsolved challenges due to its NP-hard attribute. Although many real-coded algorithms are available for TSP, they still have some performance challenges in the switch from continuous space to discrete space and perform at low convergence speed. This paper proposes a real-coded carnivorous plant algorithm with a heuristic decoding method (CPA-HDM) to solve the traveling salesman problem (TSP), which exhibits good convergence speed and solution accuracy. In this improved method, a new heuristic decoding method (HDM) is designed, which helps to map continuous variables to discrete ones without losing information, maintain population diversity, and enhance the solution quality after decoding. To balance the algorithm's search capability and enhance the probability of preferable individuals generated, an adaptive attraction probability (AAP), an improved growth model of carnivorous plants (IGMOCP), and a position update method of prey (IPUMOP) are developed. Aiming to reduce the probability of premature and prevent search stagnation, an improved reproduction strategy (IRS) and an adaptive combination perturbation are reconstructed. Finally, a local search algorithm is employed to improve the exploitation capability. To verify its validation, CPA-HDM is compared with six algorithms, for solving 28 TSP instances. The simulation results and statistical analyses demonstrate the superior performance of the proposed algorithm.

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Discrete Grey Wolf Optimizer for symmetric travelling salesman problem

Cited by 85 publications

References 34 publications

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem

Multiple Ant Colony Algorithm Combining Community Relationship Network

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

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