A multi-start ILS–RVND algorithm with adaptive solution acceptance for the CVRP

Gökalp, Osman; Uğur, Aybars

doi:10.1007/s00500-019-04072-6

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…Two standard benchmark datasets of CVRP are selected, and the results are compared with other algorithms to analyze the advantages and disadvantages of different algorithms for solving the problem. The algorithms used for comparison include SMA-CSA, ISOS, EACO [ 67 ], LNS-ACO [ 68 ], ILS–RVND [ 69 , 70 ], GRELS [ 71 ], AGES [ 72 ], and HGPSO [ 73 ].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The good results of SMA-CSA in benchmark functions (F1–F7) show the performance of the SMA-CSA algorithm in terms of exploitation and local optimum avoidance. Moreover, the algorithm is applied to the capacitated vehicle routing problem (CVRP) and compared to other algorithms such as ISOS, EACO [ 67 ], LNS-ACO [ 68 ], ILS–RVND [ 69 , 70 ], GRELS [ 71 ], AGES [ 72 ], and HGPSO [ 73 ], the results prove SMA-CSA has better optimization results and a more robust mean than other algorithms.…”

Section: Discussionmentioning

confidence: 99%

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Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Zhang

Liu

Bai³

2023

PLoS ONE

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In this article, an improved slime mould algorithm (SMA-CSA) is proposed for solving global optimization and the capacitated vehicle routing problem (CVRP). This improvement is based on the mixed-strategy optimization of Cauchy mutation and simulated annealing to alleviate the lack of global optimization capability of the SMA. By introducing the Cauchy mutation strategy, the optimal solution is perturbed to increase the probability of escaping from the local extreme value; in addition, the annealing strategy is introduced, and the Metropolis sampling criterion is used as the acceptance criterion to expand the global search space to enhance the exploration phase to achieve optimal solutions. The performance of the proposed SMA-CSA algorithm is evaluated using the CEC 2013 benchmark functions and the capacitated vehicle routing problem. In all experiments, SMA-CSA is compared with ten other state-of-the-art metaheuristics. The results are also analyzed by Friedman and the Wilcoxon rank-sum test. The experimental results and statistical tests demonstrate that the SMA-CSA algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the capacitated vehicle routing problem demonstrate its efficiency and discrete solving ability.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Zhang

Liu

Bai³

2023

PLoS ONE

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“…In recent years MS-ILS algorithms have been proposed by several authors to solve different types of optimization problems, including the two-echelon routing problem (Nguyen et al, 2012), the periodic vehicle routing problem (Michallet et al, 2014), the mixed fleet vehicle routing problem (Sassi et al, 2015), the generalized quadratic multiple knapsack problem (Avci & Topaloglu, 2017), the uncapacitated single allocation hub location problem (Guan et al, 2018), the covering salesman problem (Venkatesh et al, 2019) and the capacitated vehicle routing problem (Gokalp & Ugur, 2020). However, no research was found of MS-ILS as a proposed method to solve the MRCPSP.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi‐start iterated local search metaheuristic for the multi‐mode resource‐constrained project scheduling problem

2021

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In this paper a multi-start iterated local search (MS-ILS) algorithm is presented as a new and effective approach to solve the multi-mode resource-constrained project scheduling problem (MRCPSP). The MRCPSP is a well-known project scheduling NP-Hard optimization problem, in which there is a trade-off between the duration of each project activity and the amount of resources they require to be completed. The proposed algorithm generates an initial solution, performs a local search to obtain a local optimum, subsequently, for a certain number of iterations, makes a perturbation to that local optimum and performs a new local search on the perturbed solution.This whole process then restarts with a different initial solution for a certain number of restarts. The algorithm was tested on benchmark instances of projects with 30, 50 and 100 activities from well-known libraries. The obtained results were compared to recent benchmark results from the literature. The proposed algorithm outperforms other solution methods found in related literature for the largest tested instances (100 activities), while for smaller instances it shows to be quite competitive, in terms of the average deviation against known lower bounds.

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“…The − value method is used to assign the corresponding vehicles to each transport station. The error function is defined as = − ∑ ∈ (17) ℎ is the staging area, is the number of vehicles allocated, and is the proportion of waste to the total. After calculation, the vehicle allocation and the resulting error are shown in Table 4.…”

Section: Optimization Of Vehicle Allocationmentioning

confidence: 99%

“…For example, Lucía Cazabal-Valencia analyzed CVRP with ellipsoidal distances, which included an inventory model with uniformly distributed demands [15]; F.E. Zulvia proposed a hybrid ant-colony optimization and genetic algorithm for solving CVRP with a time window and fuzzy travel time and demand [16]; Osman Gokalp proposed a novel algorithm based on iterated local search and the random variable neighborhood descent metaheuristic method for the purpose of solving CVRP [17]; Mahmuda Akhtar presented a modified backtracking search algorithm in CVRP models, with the smart bin concept to find the best optimized waste collection path distances [18]; Sami Faiz developed a decision support system for solving CVRP that integrated GIS enriched by a tabu search model [19]; Chengming Qi proposed a two-stage hybrid Ant Colony System (ACS) algorithm for CVRP that minimized the number of vehicles used and travel cost [20]; A. Gomez presented a new artificial bee colony algorithm for solving CVRP [21]; M. Ammi and S. Chikhi proposed an island model for solving CVRP, which consists of using a paradigm, called the island model, that rules the cooperation held by different islands [22]; S.L. Gadegaard proposed a new polynomially sized formulation of the well-known symmetric CVRP [23]; Yiyong Xiao presented a mathematical optimization model to formally characterized the fuel consumption rate considered in CVRP [24]; Rodrigo Linfati proposed a heuristic algorithm for the reoptimization of CVRP in which the number of customers increases, which uses the proposed performance to reduce route dispersion and minimize length [25]; Jiashan Zhang presented a novel two-phase heuristic approach for the CVRP to overcome limitation [26]; Ali Asghar Rahmani Hosseinabadi introduced a new metaheuristic optimization algorithm to solve CVRP that is based on the law of gravity and group interactions [27]; Asma M. Altabeeb proposed a new hybrid firefly algorithm to solve CVRP [28]; Hadi Karimi investigated various stabilization techniques for improving the column generation algorithm and proposed a novel stabilization technique specialized for CVRP [29]; A.K.M.…”

Section: Introductionmentioning

confidence: 99%

Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm

Liu

Chen

et al. 2020

IJERPH

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In response to the emergent public health event of COVID-19, the efficiency of transport of medical waste from hospitals to disposal stations is a worthwhile issue to study. In this paper, based on the actual situation of COVID-19 and environmental impact assessment guidelines, an immune algorithm is used to establish a location model of urban medical waste storage sites. In view of the selection of temporary storage stations and realistic transportation demand, an efficiency-of-transport model of medical waste between hospitals and temporary storage stations is established by using an ant colony–tabu hybrid algorithm. In order to specify such status, Wuhan city in Hubei Province, China—considered the first city to suffer from COVID-19—was chosen as an example of verification; the two-level model and the immune algorithm–ant colony optimization–tabu search (IA–ACO–TS) algorithm were used for simulation and testing, which achieved good verification. To a certain extent, the model and the algorithm are proposed to solve the problem of medical waste disposal, based on transit temporary storage stations, which we are convinced will have far-reaching significance for China and other countries to dispatch medical waste in response to such public health emergencies.

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A multi-start ILS–RVND algorithm with adaptive solution acceptance for the CVRP

Cited by 11 publications

References 26 publications

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Multi‐start iterated local search metaheuristic for the multi‐mode resource‐constrained project scheduling problem

Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm

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