Biogeography-Based Optimization: A 10-Year Review

Ma, Haiping; Simon, Dan; Siarry, Patrick; Yang, Zhile; Fei, Minrui

doi:10.1109/tetci.2017.2739124

Cited by 127 publications

(62 citation statements)

References 178 publications

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“…The BBO algorithm uses a special vocabulary like that in biogeography, where a habitat (island) denotes a candidate solution of the problem, Suitability-Index-Variables (SIV) represent the features of the solution (SIV refers to the number of tuned parameters, in MF-SMC it is λ and k), and all solutions are evaluated and its quality represent the Habitat-Suitability-Index (HSI) which is similar to the fitness or cost in other population-based optimization algorithms [18]. BBO includes two steps: migration (information sharing) and mutation [19].…”

Section: Bbo Algorithmmentioning

confidence: 99%

Design of Model Free Sliding Mode Controller based on BBO Algorithm for Heart Rate Pacemaker

Karam¹,

Tashan²,

Mohsin³

2019

IJMECS

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A modified Model-Free Sliding Mode Controller (MF-SMC) scheme, which based on the Biogeography-Based Optimization (BBO) algorithm is suggested, to regulate the heart rate since it is difficult to derive a suitable model for the control algorithm, therefore the traditional control algorithm cannot perform efficiently. Also, a smoothing boundary layer is used to eliminate the chattering in the MF-SMC control signal. Results of simulation effort show that overshoot less and fast performance is achieved with stable convergence for the tracking regardless of the heart rate model to be controlled.Index Terms-Model free controller, SMC controller, BBO algorithm, heart rate (HR), pacemaker. 32Design of Model Free Sliding Mode Controller based on BBO Algorithm for Heart Rate Pacemaker

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Section: Bbo Algorithmmentioning

confidence: 99%

Design of Model Free Sliding Mode Controller based on BBO Algorithm for Heart Rate Pacemaker

Karam¹,

Tashan²,

Mohsin³

2019

IJMECS

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“…6.4 Biogeography Based Optimization (BBO): [15] BBO is a population-based evolutionary algorithm that is based on the mathematics of biogeography. In BBO, problem solutions are analogous to islands, and the sharing of features between solutions is analogous to the migration of species.…”

Section: Fig12: Pseudo Code For Intelligent Water Drops Algorithmmentioning

confidence: 99%

Enhanced Resource Provisioning Strategies for Scientific Workflows in Cloud Environment: A Survey

Sridevi¹,

Katiravan²

2018

IJCA

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Efficient resource provisioning is the key challenge that brings the best quality of service which is beneficial both to the users and CSPs. Since cloud computing aims at providing adaptive provisioning as pay-per-use basis, dynamic resource provisioning is a critical research issue. The on-demand provisioning and resource availability in cloud computing make it ideal for executing scientific workflow applications. To ensure better performance, there is a need for auto-scaling. The problem of assigning resources to tasks and orchestrating their execution to preserve the dependencies of workflows is NP-complete. [2] Hence, no optimal solution can be found in polynomial time. NP-complete problems are often addressed by using heuristic or meta-heuristics approaches. This survey presents the existing auto-scaling techniques and metaheuristics approaches for VM placements of cloud workflows.

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“…And there are some well-known evolutionary algorithms inspired by GA, such as biogeography based optimization [5,6] and genetic swarm optimization [7]. In our previous work, we used GA to optimize the DIPPS in fuzzy environment [8].…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

Zhang

et al. 2017

Mathematical Problems in Engineering

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Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

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Biogeography-Based Optimization: A 10-Year Review

Cited by 127 publications

References 178 publications

Design of Model Free Sliding Mode Controller based on BBO Algorithm for Heart Rate Pacemaker

Design of Model Free Sliding Mode Controller based on BBO Algorithm for Heart Rate Pacemaker

Enhanced Resource Provisioning Strategies for Scientific Workflows in Cloud Environment: A Survey

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

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