A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

doi:10.1155/2016/5803893

Cited by 56 publications

(22 citation statements)

References 28 publications

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“…Findings of their study revealed that the proposed algorithm was less time consuming and outperformed other methods in terms of solving real-life instances and random instances with high quality. In order to solve the QAP, Lim et al (2016) proposed another hybrid algorithm which is formed by combining the Biogeography-Based Optimization Algorithm and Tabu Search. With the use of the proposed hybrid algorithm, the best solutions were found for 36 instances out of 37 instances.…”

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

“…By means of abstracting a species migration mechanism in different islands, these Biogeography-based optimization methods mimic the distribution of the species in natural biogeography. The island in the BBO [14] is not only a small piece of land surrounded by water but an area that is isolated geographically. In biogeography, the quality of geographical areas can be judged by taking various elements into consideration that include humidity, temperature, and climate.…”

Section: Proposed Biogeography-based Optimization (Bbo) -Adhoc On-mentioning

confidence: 99%

A Biogeography-Based Optimization (BBO) With AOMDV for Mitigating Black Hole Attacks

2020

IJRTE

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 Abstract: The availability of smaller, cheaper and a lot more powerful mobile devices has made the Mobile Ad Hoc Networks (MANETs) emerge as a fast-growing research area. A Black Hole Attack can harm the mobile nodes by giving false replies to its source which can have the path that is the shortest to the destination without it checking the routing table. The primary aspect relaying the strategy of the trust route was the generation of a route by the selection of nodes that have high trust; thus, the nodes having the maximum trust measure with a better probability of routing success. One of the recently proposed metaheuristic that is applied successfully to various problems of optimization owing to its efficiency and simplicity is the Biogeography-based optimization (BBO). The BBO generally performs well for the problems of low-dimensional optimization but the performance keeps deteriorating rapidly for high-dimensional problems. In this work, proposed a BBO -Ad-hoc on Demand Multipath Distance Vector (AOMDV) method to mitigate black hole attack.

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“…However, an application of BBO algorithm for machine layout design problems to minimize the material handling distance is described by Sooncharoen et al (2015). A hybrid technique is proposed combining BBO and TS algorithms to solve QAP and obtained better results within reasonable computational time by Lim et al (2016). Regarding multi-objective BBO, some of its applications are depicted here.…”

Section: Application Of Bbo and Nsbbo From Literaturementioning

confidence: 99%

Multi-objective facility layout problems using BBO, NSBBO and NSGA-II metaheuristic algorithms

Singh¹,

Ingole²

2019

10.5267/j.ijiec

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Quantitative and qualitative objectives are both significant to solve any facility layout problem (FLP), which is called as multi-objective FLP. Generally, quantitative factors are considered as material handling cost, time, etc., and qualitative factors are considered as closeness rating, hazardous movement between the facilities, etc. For solving and optimizing two or more objectives, two methods are available. First is weight approach method and second is nondominated sorting method. In the former method, suitable weights are given to each objective and combined in a single objective function; while in later method, the objectives are defined separately and by making comparison of the solutions on the non-dominance criteria, best Pareto-optimal solutions are obtained. In this paper, equal area multi-objective FLPs which are formulated as quadratic assignment problem (QAP) are considered and optimized using biogeography based optimization (BBO) algorithm and non-dominated sorting BBO (NSBBO) algorithm. BBO is one of the efficient metaheuristic techniques, developed to solve complex optimization problems. Computational results of BBO algorithm using weight approach illustrate its better performance compared to other methods while solving multi-objective FLPs. Furthermore to obtain Pareto optimal solutions, NSBBO algorithm is implemented.

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A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem

Cited by 56 publications

References 28 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

A Biogeography-Based Optimization (BBO) With AOMDV for Mitigating Black Hole Attacks

Multi-objective facility layout problems using BBO, NSBBO and NSGA-II metaheuristic algorithms

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