Titlebook: Brain Storm Optimization Algorithms; Concepts, Principles Shi Cheng,Yuhui Shi Book 2019 Springer Nature Switzerland AG 2019 Computational I

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Reduced-Rank Local Distance Metric Learningmmunity. However, like many other population based algorithms, BSO shows good performance at global exploration but not good enough at local exploitation. To alleviate this issue, in this chapter, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is utilized in the Global-best BSO (GBSO),

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Towards Description of Block Model on Graphcorrelation relation among different parameters for the conditional maintenance of the aircraft. However, the high-dimensional and continuous features in the real number field bring challenges to the extraction algorithms for flight data. Brain Storm Optimization (BSO) algorithm can acquire the opti

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Frank Hutter,Kristian Kersting,Isabel Valeraation approach based on the brainstorming process, which is referred as StormOptimus. The single objective optimization framework is utilized for sizing of four amplifiers, and one VLSI power grid circuit. During optimization, the problem specific information required for each circuit is kept to min

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https://doi.org/10.1007/978-3-030-67664-3lutionary algorithms (EAs) are very promising approaches for the scheduling problems due to its dynamic characteristics, multiple contradicting objectives and highly nonlinear constraints. Brain storm optimization (BSO) algorithm and its variations are new emerging evolutionary algorithms. In this c

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Permutation Structure in 0-1 Dataor slightly over loaded conditions. Maintaining voltage stability is the one of the major factor for power system networks. In this chapter voltage stability has been examined by using Flexible Alternating Current Transmission System (FACTS) devices. These devices enhance the stability of the system

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Brain Storm Optimization Algorithms978-3-030-15070-9Series ISSN 1867-4534 Series E-ISSN 1867-4542

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Adaptation, Learning, and Optimizationhttp://image.papertrans.cn/b/image/190235.jpg