High-Level Relay Hybrid Metaheuristic Method for Multi-Depot Vehicle Routing Problem with Time Windows

Noori, Siamak; Ghannadpour, Seyed Farid

doi:10.1007/s10852-011-9171-3

Cited by 12 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This high-level hybrid improves the GA and ACO separately to enhance its efficiency and effectiveness. The work by Noori and Ghannadpour [45] adopt three levels of the highlevel relay optimization process. The Genetic Algorithm (GA) [37] serves as the main optimization algorithm and Tabu Search (TS) [8] as an improvement method.…”

Section: Figure 2 Population-based Meta-heuristic Algorithm Implementationsmentioning

confidence: 99%

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Zamli

Kader

Azad

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

Henry Gas Solubility Optimization Algorithm (HGSO) is a recently developed population-based metaheuristic algorithm in the literature. One notable feature of HGSO is that the algorithm divides its (single) population into a set of clusters that are individually mapped to an independent HGSO with its parameter settings (as well as its local best). At a glance, having multiple independent HGSO serving the given clusters in the population can definitely boost exploration (i.e., in terms of roaming the new potential region in the search space for better solution alternatives). However, a closer look reveals two main limitations. Firstly, HGSO-to-cluster mapping is statically defined. To be specific, the defined HGSO-to-cluster mapping does not consider its adaptive performance for the subsequent iteration. Secondly, HGSO implementation ignores the opportunity for hybridization with other meta-heuristic algorithms. With hybridization, one can compensate the limitation of a host algorithm with other algorithms' strength. Best results in the literature have often been associated with hybridization. Addressing these limitations, this paper proposes the development of Hybrid HGSO (HHGSO). Taking HGSO as the host algorithm, HHGSO is hybridized with four recently developed meta-heuristic algorithms, including Jaya Algorithm (JA), Sooty Tern Optimization Algorithm (STOA), Butterfly Optimization Algorithm (BOA) and Owl Search Algorithm (OSA). The individual mapping of each algorithm is made dynamic based on penalized and reward adaptive probability. Comparative performance of HHGSO with the aforementioned algorithms is conducted with a well-known Searchbased Software Engineering (SBSE) problem involving team formation problem. Additionally, the defined hybridization approach has also been adopted as a hybridization template for solving the combinatorial test generation problem with the same meta-heuristic algorithm combinations. Comparative performance is also undertaken against recently developed hyper-heuristic algorithms involving Exponential Monte Carlo with Counter, Modified Choice Function, Improvement Selection Rules, and Fuzzy Inference Selection. Our results indicate that the HHGSO hybridization has usefully improved the performance of the original HGSO and gives superior performance against the given competing algorithms.

show abstract

Section: Figure 2 Population-based Meta-heuristic Algorithm Implementationsmentioning

confidence: 99%

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Zamli

Kader

Azad

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…The benchmark problems of MDVRPTW from literatures 29,30 are selected to examine the performance of the proposed IACO in this article. These problems include the number of available vehicles, Euclidean distances among customers and normalized vehicle speeds making traveling times, and Euclidean distances numerically identical.…”

Section: The Classical Mdvrptwmentioning

confidence: 99%

“…30 These instances and the best known solutions are available at http://www.bernabe. dorronsoro.es/vrp/.…”

Section: The Classical Mdvrptwmentioning

confidence: 99%

Automobile chain maintenance parts delivery problem using an improved ant colony algorithm

Gao

Feng

et al. 2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The mode of automotive maintenance chain is gaining more and more attention in China because of its advantages such as the lower cost, higher speed, higher availability, and strong adaptability. Since the automobile chain maintenance parts delivery problem is a very complex multi-depot vehicle routing problem with time windows, a virtual center depot is assumed and adopted to transfer multi-depot vehicle routing problem with time windows to multi-depot vehicle routing problem with the virtual central depot, which is similar to a vehicle routing problem with time windows. Then an improved ant colony optimization with saving algorithms, mutation operation, and adaptive ant-weight strategy is presented for the multi-depot vehicle routing problem with the virtual central depot. At last, the computational results for benchmark problems indicate that the proposed algorithm is an effective method to solve the multi-depot vehicle routing problem with time windows. Furthermore, the results of an automobile chain maintenance parts delivery problem also indicate that the improved ant colony algorithm is feasible for solving this kind of delivery problem.

show abstract

“…HGA_ALS is executed ten times for each instance. The results obtained by HGA_ALS are compared with those obtained by three versions of tabu search: the improved tabu search algorithm (TS) proposed by Cordeau et al (2004), the sequential iterated tabu search algorithm (SITS) proposed by Cordeau and Maischberger (2012), and the hybridization of genetic algorithm and tabu search algorithm (GA+TS) proposed by Noori and Ghannadpour (2012). The results are shown in Table 4, where gap%=(the best solution obtained by HGA_ALS -BKS)/BKS.…”

Section: Hga_als Applied To Mdvrptwmentioning

confidence: 99%

“…Vidal et al (2013) designed a hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time windows, and apply it to MDVRPTW. Noori and Ghannadpour (2012) adopted a hybridization of genetic algorithm and tabu search for MDVRPTW.…”

Section: Introductionmentioning

confidence: 99%

Multi-depot vehicle routing problem with time windows under shared depot resources

Pardalos

2014

J Comb Optim

View full text Add to dashboard Cite

A new variant of multi-depot vehicle routing problem with time windows is studied. In the new variant, the depot where the vehicle ends is flexible, namely, it is not entirely the same as the depot that it starts from. An integer programming model is formulated with the minimum total traveling cost under the constrains of time window, capacity and route duration of the vehicle, the fleet size and the number of parking spaces of each depot. As the problem is an NP-Hard problem, a hybrid genetic algorithm with adaptive local search is proposed to solve it. Finally, the computational results show that the proposed method is competitive in terms of solution quality. Compared with the classic MDVRPTW, allowing flexible choice of the stop depot can further reduce total traveling cost.

show abstract

High-Level Relay Hybrid Metaheuristic Method for Multi-Depot Vehicle Routing Problem with Time Windows

Cited by 12 publications

References 42 publications

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Automobile chain maintenance parts delivery problem using an improved ant colony algorithm

Multi-depot vehicle routing problem with time windows under shared depot resources

Contact Info

Product

Resources

About