Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

Pierre, Djamalladine Mahamat; Zakaria, Nordin

doi:10.1016/j.asoc.2016.09.039

Cited by 41 publications

(21 citation statements)

References 23 publications

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“…Although Genetic Algorithms (GAs) are a powerful global search tool, they are also known to be weak at fine‐tuning a solution which is already close to a minimum/maximum (Harkte ; Sun and Verschuur ; Pierre and Zakaria ). In order to overcome this weakness, we introduce a dedicated fine‐tuning scheme into the efficiency‐improved GA (eGA) to further improve its convergence speed.…”

Section: An Efficiency‐improved Genetic Algorithmmentioning

confidence: 99%

“…Chuang, Chen and Hwang () proposed a real‐coded genetic algorithm using ranking selection, direction‐based crossover and the dynamic random mutation for constrained optimization problems. Pierre and Zakaria () introduced a stochastic partially optimized cyclic shift crossover operator, which emulated the hill‐climbing mechanism, to improve the local search capability of GAs in the vehicle routing problem. Sajeva et al .…”

Section: Introductionmentioning

confidence: 99%

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An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

Acuna

Sun²

2020

Geophysical Prospecting

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A B S T R A C TAlthough Genetic Algorithms have found many successful applications in the field of exploration geophysics, the convergence speed remains a big challenge as Genetic Algorithms usually require a huge amount of fitness function evaluations. In this paper, we propose an efficiency-improved Genetic Algorithm, which has both a good global search capability and a good local search capability, and is also capable of robustly handling the premature convergence challenge commonly seen in linear and directed non-linear optimization methods. In our new genetic algorithm, the global search capability is performed via a modified island model, while the local search capability is provided by a novel self-adaptive differential evolution fine tuning scheme. Premature convergence is dealt with via a local exhaustive search method. We first demonstrate the much improved convergence speed of this efficiency-improved Genetic Algorithm over that of our previously proposed advanced Genetic Algorithm on several multimodal functions. We further demonstrate the effectiveness of our efficiency-improved Genetic Algorithm by applying it to a two-dimensional common reflection surface stacking problem, which is a highly nonlinear geophysical optimization problem, to obtain very encouraging results.

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Section: An Efficiency‐improved Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

Acuna

Sun²

2020

Geophysical Prospecting

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“…Genetic algorithms, as meta-heuristics, are proven to provide near-optimal solutions to complex optimization problems in an acceptable time [13]. Furthermore, the ability to maintain a population of candidate solutions in the calculation process makes them suitable to approximate Pareto-optimal sets of multi-objective problems [13]. Based on these reasons, a hybrid multi-objective genetic algorithm is designed for BOTWAVRP.…”

Section: Hybrid Multi-objective Genetic Algorithmmentioning

confidence: 99%

“…Initialization is the first operator of the optimization process. One goal of this operator is to provide a good approximation of search space so that the solution of MOGA can converge into the good basins of the solutions space [13]. Besides, it is to help MOGAs rapidly generate an initial population.…”

Section: Initializationmentioning

confidence: 99%

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A Hybrid Multi-objective Genetic Algorithm for Bi-objective Time Window Assignment Vehicle Routing Problem

Lü²,

Ma³

2019

PROMET

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Providing a satisfying delivery service is an important way to maintain the customers’ loyalty and further expand profits for manufacturers and logistics providers. Considering customers’ preferences for time windows, a bi-objective time window assignment vehicle routing problem has been introduced to maximize the total customers’ satisfaction level for assigned time windows and minimize the expected delivery cost. The paper designs a hybrid multi-objective genetic algorithm for the problem that incorporates modified stochastic nearest neighbour and insertion-based local search. Computational results show the positive effect of the hybridization and satisfactory performance of the metaheuristics. Moreover, the impacts of three characteristics are analysed including customer distribution, the number of preferred time windows per customer and customers’ preference type for time windows. Finally, one of its extended problems, the bi-objective time window assignment vehicle routing problem with time-dependent travel times has been primarily studied.

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A Brief Literature Review About Bioinspired Metaheuristics to Solve Vehicle Routes Problem with Time Window

Polo-Pichon

Palacio

De-la-Hoz-Franco

2022

Proceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021)

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The Vehicle Routing Problem with Time Windows has become a pretty studied optimization problem. Because the time constraint greatly expands the possible solutions; this allow that the model get closer to reality. In this investigation, the restrictions, the conclusions and future works of around two hundred papers, have been consulted at Web of Science, Scopus and Sage. In these, the most widely used bioinspired metaheuristic algorithms were analyzed to provide solutions in the vehicle routing problem with time windows. The objective of this research is the publication of an article, which serves as a reference or support to give a broad documented vision to future research on Bioinspired Metaheuristics. The findings about them: Few written evidence it found about periodic restriction, and only some papers, involving heterogeneous fleets. From the perspective of metaheuristic techniques, the algorithms that has been most used for solve this problem, are the genetic algorithms. In addition, little information was found on the application of the Buffalo and Cuckoo algorithms. Which can become a great opportunity for new research in this field. Based on these findings, it can be concluded that the application of hybrid algorithms, could be analyzed in depth. Because there is little information, and this represents a wide and interesting spectrum of research, which can be used to increase solutions in these problems.

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Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

Cited by 41 publications

References 23 publications

An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

A Hybrid Multi-objective Genetic Algorithm for Bi-objective Time Window Assignment Vehicle Routing Problem

A Brief Literature Review About Bioinspired Metaheuristics to Solve Vehicle Routes Problem with Time Window

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