A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem

Chen, Shifeng; Chen, Rong; Gao, Jian

doi:10.3390/a10030107

Cited by 44 publications

(23 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To avoid the proposed algorithm falling into the local optimal solution, a modified butterfly adjusting operator is used as a mutation operator in ABC. Chen et al [10] introduced MBO with the greedy strategy to solve the dynamic vehicle routing problem (DVRP), which is a transformed version of a VRP with dynamic customer appearance. The introduced algorithm outperformed the existing methods while drawing several new best solutions.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…After the operator application, the fitness values of each generated individual are evaluated. If the newly generated individual has a better fitness value than the previous one, the old parent individual is replaced with the new one via the greedy strategy, as in Equation (11) [10].…”

Section: Monarch Butterfly Optimizationmentioning

confidence: 99%

“…The simple structure of the algorithm facilitates the implementation of complicated expressions of the layout representation in computer code. In addition, several studies have successfully demonstrated MBO's performance [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path

Kim

Chae

2019

Mathematics

View full text Add to dashboard Cite

Facility layout problems (FLPs) are concerned with the non-overlapping arrangement of facilities. The objective of many FLP-based studies is to minimize the total material handling cost between facilities, which are considered as rectangular blocks of given space. However, it is important to integrate a layout design associated with continual material flow when the system uses circulating material handling equipment. The present study proposes approaches to solve the layout design and shortest single loop material handling path. Monarch butterfly optimization (MBO), a recently-announced meta-heuristic algorithm, is applied to determine the layout configuration. A loop construction method is proposed to construct a single loop material handling path for the given layout in every MBO iteration. A slicing tree structure (STS) is used to represent the layout configuration in solution form. A total of 11 instances are tested to evaluate the algorithm’s performance. The proposed approach generates solutions as intended within a reasonable amount of time.

show abstract

Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Monarch Butterfly Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path

Kim

Chae

2019

Mathematics

View full text Add to dashboard Cite

show abstract

“…Chen et al [4] proposed a new variant of MBO by introducing a greedy strategy to solve dynamic vehicle routing problems (DVRPs). In contrast to the basic MBO algorithm, the proposed algorithm accepted only butter y individuals that had better tness than before implementation of the migration and butter y adjusting operator.…”

Section: Mbo Algorithmmentioning

confidence: 99%

Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

Wang

2019

Open Geosciences

View full text Add to dashboard Cite

China has one of the highest rates of natural disasters in the world. In recent years, the Chinese government has placed a high value on improving emergency natural disaster relief. The goal of this research was to resolve a key issue for emergency natural disaster relief: the emergency vehicle routing problem (EmVRP) with relief materials in sudden disasters. First, we provided a description of the EmVRP, and de ned the boundary conditions. On this basis, we constructed an optimization model of EmVRP with relief materials in sudden disasters. To reach the best solution in the least amount of time, we proposed an enhanced monarch butter y optimization (EMBO) algorithm, incorporating two modi cations to the basic MBO: a self-adaptive strategy and a crossover operator. Finally, the EMBO algorithm was used to solve the EmVRP. Our experiments using two examples EmVRP with relief materials in a sudden-onset disaster proved the suitability of EMBO. In addition, an array of comparative studies showed that the proposed EMBO algorithm can achieve satisfactory solutions in less time than the basic MBO algorithm and seven other intelligent algorithms.

show abstract

“…Chen et al [107] proposed a new variant of MBO by introducing a greedy strategy to solve dynamic vehicle routing problems (DVRPs). In contrast to the basic MBO algorithm, the proposed algorithm accepted only butterfly individuals that had better fitness than before implementation of the migration and butterfly adjusting operators.…”

Section: Related Workmentioning

confidence: 99%

Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population

Cai

et al. 2018

Algorithms

View full text Add to dashboard Cite

Abstract:Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to the parameter p, which is unchanged during the entire optimization process. In our present work, a self-adaptive strategy is introduced to dynamically adjust the butterflies in land 1 and 2. Accordingly, the population size in subpopulation 1 and 2 are dynamically changed as the algorithm evolves in a linear way. After introducing the concept of a self-adaptive strategy, an improved MBO algorithm, called monarch butterfly optimization with self-adaptive population (SPMBO), is put forward. In SPMBO, only generated individuals who are better than before can be accepted as new individuals for the next generations in the migration operation. Finally, the proposed SPMBO algorithm is benchmarked by thirteen standard test functions with dimensions of 30 and 60. The experimental results indicate that the search ability of the proposed SPMBO approach significantly outperforms the basic MBO algorithm on most test functions. This also implies the self-adaptive strategy is an effective way to improve the performance of the basic MBO algorithm.

show abstract

A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem

Cited by 44 publications

References 31 publications

Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path

Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path

Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population

Contact Info

Product

Resources

About