Modified artificial bee colony for the vehicle routing problems with time windows

Alzaqebah, Malek; Abdullah, Salwani; Jawarneh, Sana

doi:10.1186/s40064-016-2940-8

Cited by 38 publications

(30 citation statements)

References 31 publications

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“…Next, the performance of the proposed algorithm was compared in each problem instance of the Solomon benchmark dataset. The compared algorithm was taken from [19] which proposed the modified bee algorithm (BA). The results are presented in this section.…”

Section: Resultsmentioning

confidence: 99%

“…Many heuristics, metaheuristics, swarm intelligence, and other algorithms have been widely published to solve the VRPTW in the past few decades. The Bee algorithm (BA) and modified artificial bee colony were published to solve the VRPTW, for example, in the publications of [17][18][19]. The artificial bee colony (ABC) algorithm is a class of natureinspired algorithm which was proposed by [20].…”

Section: Literature Reviewmentioning

confidence: 99%

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An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Kantawong

Pravesjit

2020

ECTI-CIT

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This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Kantawong

Pravesjit

2020

ECTI-CIT

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“…Alzaqebah et al [75] used a modified ABC algorithm for solving Vehicle Routing Problems with Time Windows (VRPTW). Their modification included the use of abandoned solutions by scout bees, a roulette wheel selection of candidate solutions, and the generation of new random candidate solutions to be inserted in the swarm.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

Masutti

Castro

2017

Mathematical Problems in Engineering

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Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

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“…Swarm-based algorithms are naturally inspired algorithms that mimic the group's animal behavior in searching for food, for example, The Grey Wolf Optimizer proposed by [13], Particle Swarm Optimization algorithm [14], Moth-Flame Optimization [15] and the Artificial Bee Colony algorithm [16]. Swarm-based algorithms have been widely used to solve optimization problems and showed to be superior in many fields of research [17][18][19][20].…”

mentioning

confidence: 99%

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Alzaqebah¹,

Alrefai²,

Ahmed³

et al. 2020

IJECE

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Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.

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Modified artificial bee colony for the vehicle routing problems with time windows

Cited by 38 publications

References 31 publications

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

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