A general variable neighborhood search for solving the multi-objective open vehicle routing problem

Sánchez‐Oro, Jesús; López-Sánchez, Ana Dolores; Colmenar, J. Manuel

doi:10.1007/s10732-017-9363-8

Cited by 24 publications

(11 citation statements)

References 17 publications

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“…However, till now no single criteria is available to evaluate the overall performance of MO-algorithms. Here we use two most commonly used metrics namely hyper volume (HV) and coverage metric (C-Metric) to evaluate the performance of proposed MACS [17], [34].…”

Section: Experimentation and Discussionmentioning

confidence: 99%

“…Next proposed MACS has been tested on same Solomon benchmarks while considering all the three objectives simultaneously. Since NSGA-II (Non-dominated Sorting GA) is one of the successfully used evolutionary algorithm for MOPs [34], [40], [41] so here the NSGA-II is taken as comparative algorithm for the proposed approach. The approach used by [34] has been adopted to fit NSGA-II for MO-VRPTW and accordingly a population of 200 chromosomes, and 100000 generations has been used.…”

Section: Observations For Mo-vrptwmentioning

confidence: 99%

“…Since NSGA-II (Non-dominated Sorting GA) is one of the successfully used evolutionary algorithm for MOPs [34], [40], [41] so here the NSGA-II is taken as comparative algorithm for the proposed approach. The approach used by [34] has been adopted to fit NSGA-II for MO-VRPTW and accordingly a population of 200 chromosomes, and 100000 generations has been used. The cross-over rate and probability of mutation are .6 and .3 respectively.…”

Section: Observations For Mo-vrptwmentioning

confidence: 99%

See 2 more Smart Citations

Improved Multi-Ant-colony algorithm for solving Multi-Objective Vehicle Routing Problems

Goel¹,

Maini

2019

Scientia Iranica

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Classical vehicle routing problems (VRP) involves supply of goods/services from a central depot to geographically scattered customers. Besides the classical objective of minimizing the total travelled distance, the present work also considers simultaneous optimization of two additional objectives namely minimizing make span and minimizing distance imbalance. A mathematical model has been developed to deal with this multiobjective version of VRP (MO-VRPTW). A meta-heuristic based on multiple ant colony system for solving this MO-VRPTW has also been proposed. Firefly optimization algorithm (FA) has also been applied to avoid local optima. Two new migration operators named Migration-I and Migration-II and multi-pheromone matrices have been developed to further improve the solution quality. The proposed algorithm has been tested on a number of benchmark problems and its superiority over other state of art approaches and NSGA-II is demonstrated.

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Section: Experimentation and Discussionmentioning

confidence: 99%

Section: Observations For Mo-vrptwmentioning

confidence: 99%

Section: Observations For Mo-vrptwmentioning

confidence: 99%

See 1 more Smart Citation

Improved Multi-Ant-colony algorithm for solving Multi-Objective Vehicle Routing Problems

Goel¹,

Maini

2019

Scientia Iranica

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“…A recent study conducted by Li et al [33] solved a new variant named budgeted maximum coverage problem based on an algorithm that combines reinforcement learning with tabu search. For solving VRP and its variants, efficient algorithms such as tabu search [34,35], variable neighborhood search [36], large neighborhood search [37], genetic algorithms [38], iterated local search algorithms [39,40], and hybrid algorithms [41,42] have been adopted in existing studies. However, the existing heuristic and mate-heuristic algorithms cannot be directly implemented to solve our studied problem.…”

Section: Related Workmentioning

confidence: 99%

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship

Chen

Luo

2022

Sensors

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Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.

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“…Therefore, most of the obtained solutions are not local optimum and can be improved by means of a local optimizer. The second phase of the GRASP algorithm is intended to find a local optimum of the solution generated, usually applying a local search method, although it can be replaced with a more complex optimizer, like Tabu Search or Variable Neighborhood Search, for instance [31][32][33].…”

Section: Greedy Randomized Adaptive Search Proceduresmentioning

confidence: 99%

On the Analysis of the Influence of the Evaluation Metric in Community Detection over Social Networks

et al. 2018

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Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps with analyzing groups of users with similar interests. As a consequence, it is possible to detect radicalism or even reduce the size of the data to be analyzed, among other applications. This paper presents a metaheuristic approach based on Greedy Randomized Adaptive Search Procedure (GRASP) methodology for detecting communities in social networks. The community detection problem is modeled as an optimization problem, where the objective function to be optimized is the modularity of the network, a well-known metric in this scientific field. The results obtained outperform classical methods of community detection over a set of real-life instances with respect to the quality of the communities detected.

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A general variable neighborhood search for solving the multi-objective open vehicle routing problem

Cited by 24 publications

References 17 publications

Improved Multi-Ant-colony algorithm for solving Multi-Objective Vehicle Routing Problems

Improved Multi-Ant-colony algorithm for solving Multi-Objective Vehicle Routing Problems

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship

On the Analysis of the Influence of the Evaluation Metric in Community Detection over Social Networks

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