DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

Gong, Wenyin; Cai, Zhihua; Ling, Charles X.

doi:10.1007/s00500-010-0591-1

Cited by 330 publications

(143 citation statements)

References 47 publications

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“…As a comparison between the four original models of BBO (PMB, SMB, SPMB and SSMB), BBO gives the best performance when the given problem is hard, has large upper and lower dimensional and/or the number of islands or population BBO lacks the exploration [18]. Therefore, in this study, the root problem Original ver.…”

Section: Discussionmentioning

confidence: 99%

Essential Modifications on Biogeography-Based Optimization Algorithm

Al-Roomi¹,

Albasri²,

Talaq³

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications were employed to enhance its performance. This paper proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process after the mutation stage is sized to avoid any corruption on the suitability index variables. The obtained results through wide variety range of test functions with differentdimensions and complexities proved that the BBO performance can be enhanced effectively without using any complicated form of the immigration and emigration rates. This essential modification has to be considered as an initial step for any other modification.

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Section: Discussionmentioning

confidence: 99%

Essential Modifications on Biogeography-Based Optimization Algorithm

Al-Roomi¹,

Albasri²,

Talaq³

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Recently, many studies hybridize two or more algorithms to obtain optimal solutions for optimization problems [31]. Some of these studies hybridize differential evolution with biogeographybased optimization to solve global optimization problem [8]. Some hybridize particle swarm optimization with differential evolution for solving constrained numerical and engineering optimization problems [19].…”

Section: B Gehad Ismail Sayedmentioning

confidence: 99%

A hybrid SA-MFO algorithm for function optimization and engineering design problems

Sayed

Hassanien

2018

Complex Intell. Syst.

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This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

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“…Gong et al (2010) proposed a hybrid DE/BBO algorithm, which combines the exploration of DE with the exploitation of BBO effectively. The core idea is to hybridize DE's mutation operator with BBO's migration operator, such that good solutions would be less destroyed, while poor solutions can accept a lot of new features from good solutions.…”

Section: Related Workmentioning

confidence: 99%

“…As described in the related work, the DE/BBO (Gong et al 2010) balances the exploration of DE with the exploitation of BBO by hybridizing DE mutation with BBO migration. In order to show that the local topologies can also improve the state-of-the-art hybrid BBO, we develop three new versions of DE/BBO by introducing the ring, square, and random topologies, denoted by RingDB, SquareDB, and RandDB respectively.…”

Section: Comparative Experiments For De/bbo With Global and Local Topmentioning

confidence: 99%

“…We introduce three local topologies, namely the ring topology, the square topology, and the random topology, to BBO, and demonstrate that our approach can achieve significant performance improvement. We also test the local topologies on a hybrid algorithm, namely DE/BBO (Gong et al 2010), and show that our approach is also useful to improve the state-of-the-art method. To our best knowledge, the paper is the first attempt to improve BBO by modifying its internal structure with local topologies.…”

Section: Introductionmentioning

confidence: 99%

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Localized biogeography-based optimization

Zheng

Ling

et al. 2013

Soft Comput

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Biogeography-based optimization (BBO) is a relatively new heuristic method, where a population of habitats (solutions) are continuously evolved and improved mainly by migrating features from high-quality solutions to low-quality ones. In this paper we equip BBO with local topologies, which limit that the migration can only occur within the neighborhood zone of each habitat. We develop three versions of localized BBO algorithms, which use three different local topologies namely the ring topology, the square topology, and the random topology respectively. Our approach is quite easy to implement, but it can effectively improve the search capability and prevent the algorithm from being trapped in local optima. We demonstrate the effectiveness of our approach on a set of well-known benchmark problems. We also introduce the local topologies to a hybrid DE/BBO method, resulting in three localized DE/BBO algorithms, and show that our approach can improve the performance of the state-of-the-art algorithm as well.

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DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

Abstract: Abstract-Differential Evolution (DE) is

Cited by 330 publications

References 47 publications

Essential Modifications on Biogeography-Based Optimization Algorithm

Essential Modifications on Biogeography-Based Optimization Algorithm

A hybrid SA-MFO algorithm for function optimization and engineering design problems

Localized biogeography-based optimization

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