Applying Multi-Objective Optimization Algorithms to Mechanical Engineering

Shivach, Preeti; Nautiyal, Lata; Ram, Mangey

doi:10.4018/978-1-5225-3035-0.ch014

“…Community discovery algorithm based on multi-objective optimization, which combines quantitative indicators of regional scheduling workloads, community discovery algorithms [18], and multi-objective optimization algorithms [19]. Firstly, the Fast Unfolding community discovery algorithm [20] is performed based on the similarity matrix of the site.…”

Section: Community Discovery Algorithm Based On Multi-objective Optimmentioning

confidence: 99%

Peer Review #1 of "Urban public bicycle dispatching optimization method (v0.1)"

Zhang¹

2019

0

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Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

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“…Community discovery algorithm based on multi-objective optimization, which combines quantitative indicators of regional scheduling workloads, community discovery algorithms (Shivach, Nautiyal & Ram, 2018), and multi-objective optimization algorithms (Mori & Saito, 2016). Firstly, the Fast Unfolding community discovery algorithm (Sun et al, 2018) is performed based on the similarity matrix of the site.…”

Section: Community Discovery Algorithm Based On Multi-objective Optimizationmentioning

confidence: 99%

Urban public bicycle dispatching optimization method

Lin

¹

,

Yang

²

,

Wang

³

et al. 2019

PeerJ Computer Science

2

0

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Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%–50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

show abstract

“…Community discovery algorithm based on multi-objective optimization, which combines quantitative indicators of regional scheduling workloads, community discovery algorithms [18], and multi-objective optimization algorithms [19]. Firstly, the Fast Unfolding community discovery algorithm [20] is performed based on the similarity matrix of the site.…”

Section: Community Discovery Algorithm Based On Multi-objective Optimizationmentioning

confidence: 99%

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Supplemental Information 2: NSGA2 Algorithm Code

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View full text Add to dashboard Cite

Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

show abstract

Applying Multi-Objective Optimization Algorithms to Mechanical Engineering

Cited by 6 publications

References 15 publications

Peer Review #1 of "Urban public bicycle dispatching optimization method (v0.1)"

Peer Review #1 of "Urban public bicycle dispatching optimization method (v0.1)"

Urban public bicycle dispatching optimization method

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