Parameter Selection of Discrete Particle Swarm Optimization Algorithm for the Quadratic Assignment Problems

Pradeepmon, T.G.; Panicker, Vinay V.; Sridharan, R.

doi:10.1016/j.protcy.2016.08.199

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…In this section, the review of literature is presented to show some of the several techniques that other researchers have used to solve the QAP. The Discrete Particle Swarm Optimization (DPSO) algorithm was introduced by Pradeepmon et al Sridharan (2016). In a study carried out by Pradeepmon (2018), the DPSO algorithm was modified and named Modified DPSO.…”

Section: Literature Reviewmentioning

confidence: 99%

A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem

Hameed¹,

Aboobaider²,

Mutar³

et al. 2020

10.5267/j.ijiec

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The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc. Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used. One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model. Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods. With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values. The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time. Afterwards, the proposed approach is compared with other recent methods in the literature review. Based on the computation results, the proposed hybrid approach outperforms the other methods.

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Section: Literature Reviewmentioning

confidence: 99%

A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem

Hameed¹,

Aboobaider²,

Mutar³

et al. 2020

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…The parallel algorithms are explored in Table 2. 2016 [10] Discrete Particle Swarm Optimization (DPSO) -Taguchi's robust design approach is used to select the best combination of parameters used in DPSO. Pradeepmon et al…”

Section: Introductionmentioning

confidence: 99%

Integrating the whale algorithm with Tabu search for quadratic assignment problem: A new approach for locating hospital departments

Abdel‐Basset

Manogaran

El-Shahat

et al. 2018

Applied Soft Computing

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Resolving the Quadratic Assignment Problem with the Elephant Herding Optimization

Mohsine,

Mzili,

Riffi

2024

International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023)

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Parameter Selection of Discrete Particle Swarm Optimization Algorithm for the Quadratic Assignment Problems

Cited by 9 publications

References 9 publications

A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem

A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem

Integrating the whale algorithm with Tabu search for quadratic assignment problem: A new approach for locating hospital departments

Resolving the Quadratic Assignment Problem with the Elephant Herding Optimization

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