Localized biogeography-based optimization

Zheng, Yu-Jun; Ling, Haifeng; Wu, Xiaobei; Xue, Jian

doi:10.1007/s00500-013-1209-1

Cited by 50 publications

(12 citation statements)

References 32 publications

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“…In Zheng et al [19], a novel attempt is made to integrate three different local topologies (i.e., ring, square, and random) in BBO. The new version of BBO using the local topologies is called localized BBO.…”

Section: Hybridization Of Biogeography-based Optimization With Other mentioning

confidence: 99%

A State-of-the-Art Review of Biogeography-Based Optimization

Garg

Deep

2014

Advances in Intelligent Systems and Computing

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Biogeography-based optimization (BBO) is a population-based metaheuristic evolutionary algorithm proposed by Simon (IEEE Trans Evol Comput 12 (6):702-713, 2008 [1]). It is based on the theory of island biogeography which deals with the migration, speciation, and extinction of the species in a habitat. It has excellent exploitation ability but lacks in exploration. After the inception of BBO, a lot of modifications and hybridizations are introduced to enhance its performance. This paper focuses on various modifications and refinements in the migration and mutation operators of the original BBO and its hybridization with other populationbased meta-heuristic algorithms. In the end, some open problems related to BBO are highlighted encouraging future research in this novel area.Keywords Biogeography-based optimization (BBO) Á Meta-heuristic algorithms Á Migration BackgroundOptimization is the art of choosing the best among a vast set of alternatives subject to certain constraints. Many real-life problems can be modeled as nonlinear optimization problems. It is often necessary and desirable to determine the global optimal solution of such nonlinear optimization problems. To solve these problems, the conventional deterministic methods have inherent limitations for the purpose of implementation. Hence, the probabilistic techniques have become popular due to their ease of applicability although probabilistic techniques guarantee to reach the global optima only in the probabilistic sense. Nature-inspired optimization tech-

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Section: Hybridization Of Biogeography-based Optimization With Other mentioning

confidence: 99%

A State-of-the-Art Review of Biogeography-Based Optimization

Garg

Deep

2014

Advances in Intelligent Systems and Computing

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“…In this study, we use a local random neighborhood structure [32], which randomly assigns k N neighboring solutions to each solution in the population (where k N is a parameter set to 3); if the current best solution has not been updated after a number g t of consecutive generations, the neighborhood structure is randomly reset. Algorithm 2 presents the pseudocode of the EBO algorithm, where Line 4 invokes Algorithm 1 to evaluate the fitness of each solution (initial centroid setting).…”

Section: B a Metaheuristic For Optimizing Clustersmentioning

confidence: 99%

Intelligent Optimization of Diversified Community Prevention of COVID-19 Using Traditional Chinese Medicine

Zheng

Yang

et al. 2020

IEEE Comput. Intell. Mag.

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Tr aditional Chinese medicine (TCM) has played an important role in the prevention and control of the novel coronavirus pneumonia (COVID-19), and community prevention has become the most essential part in reducing the risk of spread and protecting public health. However, most communities use a unified TCM prevention program for all residents, which violates the "treatment based on syndrome differentiation"

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“…Zheng et al [23] used three different topologies, namely, ring topology, square topology, and random topology, to enhance the exploration ability of BBO. Feng et al [24] presented a hybrid migration operator with random ring topology to enhance the potential population diversity of BBO.…”

Section: Mutation Operatormentioning

confidence: 99%

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

et al. 2020

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Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).

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Localized biogeography-based optimization

Cited by 50 publications

References 32 publications

A State-of-the-Art Review of Biogeography-Based Optimization

A State-of-the-Art Review of Biogeography-Based Optimization

Intelligent Optimization of Diversified Community Prevention of COVID-19 Using Traditional Chinese Medicine

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

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