Development of modified discrete particle swarm optimization algorithm for quadratic assignment problems

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

doi:10.5267/j.ijiec.2017.11.003

Cited by 18 publications

(20 citation statements)

References 1 publication

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“… Discrete Bat Algorithm (DBA) (Riffi et al, 2017)  Development of modified discrete particle swarm (DPSO) (Pradeepmon, 2018)  Biogeography-Based Optimization Algorithm Hybridized with Tabu Search (BBOTS) (Lim et al, 2016)  A hybrid algorithm combining lexisearch and genetic algorithms (LSGA) (Ahmed, 2018) For the compared cases in Table 6, the first comparison which was between HDDETS and DBA, it was found that the DBA can reach the optimal solution for 35 out of 54 instances and reach to Best Known Solution for 5 out of 21 instances. While the HDDETS has been solved 54 optimal solutions out of 54 instances, this implies that the gap of the best value found was 0 %.…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

“…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. This development was also aimed at solving the QAP.…”

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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show abstract

“…This algorithm adapts well to the data of the above-mentioned problem by taking Z = n. Like in the continuous version, equation 17is also used to update the particle speed. This speed, which is represented in real numbers, is subsequently transformed into a set of probabilities by equation (19). Then, the new position of the particle i is calculated by means of equations (20) and (21).…”

Section: Discrete Pso For the Optimization Of M-mdpdptwmentioning

confidence: 99%

“…The PSO algorithm has been used in various areas, namely, supply problems, (Mousavi et al, 2017), quadratic allocation problems, (Pradeepmon et al, 2018), technical design issues, (Dhiman & Kaur, 2019), as well as for solving the problems of industrial and assembly workshops (Toader, 2015).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

Dridi¹,

Alaïa²,

Borne³

et al. 2019

SIC

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This paper presents a variant of the vehicle routing problem (VRP) that combines several constraints. This variant addresses the pickup and delivery problem (PDP), the use of multiple vehicles (m), the multi-depots (MD) and the time constraint (TW): m-MDPDPTW. In the m-MDPDPTW, one should build a route made up of several pairs (customer / supplier), which starts and ends at the same depot and respects the precedence and capacity constraints. For solving this problem, two approaches based on particle swarms are proposed with a view to minimizing the total distance travelled by all vehicles. The results yielded by these two algorithms, the continuous PSO and the discrete PSO, are then compared by making use of the benchmarks generated by Li and Lim.

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“…The methods that found solutions to the QAP problem were classified into two categories as follows: the category that obtains the exact solution to QAP was called the exact methods, including the bounded dynamic branches and processes, Lagrangian-based relaxation methods, linear and quantitative programming methods. However, in these methods, the size of the problem requires a long calculation period if there are more than 30 methods [8], [9], [10], and [11]. The second category obtains the approximate solution or near the optimal solution with reasonable calculation time and are known as the approximate methods.…”

Section: Introductionmentioning

confidence: 99%

Improved Discrete Differential Evolution Algorithm in Solving Quadratic Assignment Problem for best Solutions

Hameed¹,

Aboobaider²,

Choon³

et al. 2018

ijacsa

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The combinatorial optimization problems are very important in the branch of optimization or in the field of operation research in mathematics. The quadratic assignment problem (QAP) is in the category of facilities location problems and is considered as one of the significant complex's combinatorial optimization problems since it has many applications in the real world. The QAP is involved in allocating N facilities to N locations with specified distances amid the locations and the flows between the facilities. The modified discrete differential evolution algorithm has been presented in this study based on the crossover called uniform like a crossover (ULX). The proposed algorithm used to enhance the QAP solutions through finding the best distribution of the N facilities to N locations with the minimized total cost. The employed criteria in this study for the evaluation of the algorithm were dependent on the accuracy of the algorithm by using the relative percent deviation (PRD). The proposed algorithm was applied to 41 different sets of the benchmark QAPLIB, while the obtained results indicated that the proposed algorithm was more efficient and accurate compared with Tabu Search, Differential Evolution, and Genetic algorithm.

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Development of modified discrete particle swarm optimization algorithm for quadratic assignment problems

Cited by 18 publications

References 1 publication

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

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

Improved Discrete Differential Evolution Algorithm in Solving Quadratic Assignment Problem for best Solutions

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