Ant Colony Optimization With an Improved Pheromone Model for Solving MTSP With Capacity and Time Window Constraint

Wang, Min; Ma, Tongmao; Li, Guiling; Zhai, Xue; Qiao, Sibo

doi:10.1109/access.2020.3000501

Cited by 34 publications

(19 citation statements)

References 36 publications

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“…where Δμ ij � m k�1 Δμ ij (k) defines the pheromone enhancement located on the scheduling path during this iteration and Δμ ij (k) further defines the pheromone quantity left on the path by the ant k in this iteration. e volatile coefficient ρ is used to describe the persistence of pheromone quantity [13]. Since the fixed value method cannot reflect the characteristics of the ACO algorithm, the adaptive variable value method is adopted in this paper to accelerate the convergence speed and reduce the probability of premature convergence, specifically as follows, where Iteration cur denotes the number of current iterations and Iteration max represents the number of maximum iterations:…”

Section: Traditional Ant Colony Algorithmmentioning

confidence: 99%

“…Naturally, it is easy to remind of finding the local minimum through multiple initial points and then finding the global minimum among multiple local minimum values. Based on the process proposed in [13], the optimal path considers the selection of several key factors, e.g., road transport time costs an average of the unobstructed degree to quickly obtain the optimal solution, transport cost factor average road unobstructed degree factor, and then transform the model into the standard ant colony algorithm, and finally achieve the path of the constraint conditions based on realtime updates and dynamic selection, guide choice of logistics transportation toward the optimal path, and get the optimal solution more precisely.…”

Section: Traditional Ant Colony Algorithmmentioning

confidence: 99%

“…e goal is to get the satisfactory solution or optimal solution of the global problem with an appropriate cost, which not only saves time but also meets the actual requirements of users. Because of the advantages of high efficiency and simple implementation, heuristic algorithms, e.g., grey wolf optimization (GWO) algorithm [9], chaos particle swarm optimization (CPSO) [10], genetic algorithm (GA) [11], firefly algorithm (FA) [12], and ant colony optimization (ACO) [13], have attracted wide attention in the field of optimization research and developed very fast in the recent years.…”

Section: Introductionmentioning

confidence: 99%

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Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm

Xiong

2021

Discrete Dynamics in Nature and Society

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The cold chain logistics distribution industry not only demands all goods can be timely distribution but also requires to reduce the entire logistics transportation cost as far as possible, and distribution vehicle route optimization is the key problem of cold chain logistics transportation cost calculation. The traditional optimization method spends a lot of time to search so that it is tough to find the globally optimal path approach, which results in higher distribution costs and lower efficiency. To solve the abovementioned problems, a cold logistics distribution path optimization solution, ground on an improved ant colony optimization algorithm (IACO) is formulated. Specially, other constraints, e.g., the transport time factor, transport cooling factor, and mean road patency factor, can be added to the unified IACO. Meanwhile, the updating mode of traditional pheromone is improved to limit the maximum and minimum pheromone concentration on the road and change the path selection transfer probability. The simulation results and experiment make clear that the IACO algorithm is lower than the chaotic-simulated annealing ant colony algorithm (CSAACO) and the traditional ACO algorithm in terms of convergence speed, logistics transportation distance, and logistics delivery time. At the same time, we have successfully obtained the optimal logistics distribution path, which can provide valuable reference information for improving the economic benefits of cold chain logistics enterprises.

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Section: Traditional Ant Colony Algorithmmentioning

confidence: 99%

Section: Traditional Ant Colony Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm

Xiong

2021

Discrete Dynamics in Nature and Society

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“…However, the selection of an optimization algorithm for this purpose is also the main concern. Several optimization algorithms such as GA (Genetic Algorithm) [7,8], ACO (Ant Colony Optimization) [9,10], ABC (Artificial Bee Colony) [11], PSO (Particle Swarm Optimization) [12] have been analyzed. GA uses particular generic operators like crossover, reproduction, and mutation to form a new population.…”

Section: Purpose Of the Investigationmentioning

confidence: 99%

Hybrid Local-Global Optimum Search Using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) for Waveguide Selection

et al. 2021

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Multiple beam combination in optical interferometry with concurrent measurement of intricate visibilities, around each possible baseline, is a trending research area. In this work, a hybrid method is proposed for three different waveguide arrays and several waveguides are excited simultaneously in each array. Each waveguide array acts as a beam combiner, the output of which determines the field intensity of each waveguide mode. The output intensity depends on the waveguide selected for excitation. Thus, waveguide selection is the major factor that can affect the output intensity. The main goal of this research is to provide an effective solution for the selection of waveguides, to provide high visibility and intensity at the output of the multi-beam combiner. In addition to this, the use of metaheuristic optimization algorithms to solve the problem of waveguide selection is proposed. To accomplish this, firstly, an analytical study has been conducted to analyze the performance of optimization algorithms, including PSO, FA and GSA, and then the results of these algorithms have been compared with the conventional approaches. And finally, a model of Hybrid Local-Global Optimum Search Algorithm using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) has been developed for waveguide selection. The performance of the proposed hybrid model is examined in MATLAB simulation software. The simulated outcomes are determined for PSO, FA, and GSA-based models, as well as, for the proposed hybrid model, in terms of normalized intensity, visibility, and min-max 1/SNR values. The results obtained from simulation show that the PSO and GSAbased models are giving better results, followed by FA and conventional approaches. This worked as a motivation behind using PSO and GSA together in the proposed system, resulting in higher intensity and visibility values. Thus, the proposed hybrid model is concluded to be more efficient and convenient, for selecting optimum waveguides from the array, to attain an optimum output.

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“…Although the solution outperforms these methods in terms of solution quality, it is slower than FL-MTSP. The authors in[63] addressed the MTSP withThe final version of this paper is published in Computer Science Review, https://doi.org/10.1016/j.cosrev.2021.100369 time window and proposed a hybrid solution by integrating ACO with the minimum spanning 1-tree to provide the optimal solution. ABC based approaches.…”

mentioning

confidence: 99%

A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

Cheikhrouhou¹

2021

Preprint

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The Multiple Travelling Salesman Problem (MTSP) is among the most interesting combinatorial optimization problems because it is widely adopted in real-life applications, including robotics, transportation, networking, etc.Although the importance of this optimization problem, there is no survey dedicated to reviewing recent MTSP contributions. In this paper, we aim to fill this gap by providing a comprehensive review of existing studies on MTSP. In this survey, we focus on MTSP’s recent contributions to both classical vehicles/robots and unmanned aerial vehicles. We highlight the approaches applied to solve the MTSP as well as its application domains. We analyze the MTSP variants and propose a taxonomy and a classification of recent studies.

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Ant Colony Optimization With an Improved Pheromone Model for Solving MTSP With Capacity and Time Window Constraint

Cited by 34 publications

References 36 publications

Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm

Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm

Hybrid Local-Global Optimum Search Using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) for Waveguide Selection

A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

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