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Titlebook: Advanced Optimization by Nature-Inspired Algorithms; Omid Bozorg-Haddad Book 2018 Springer Nature Singapore Pte Ltd. 2018 Pattern Search (

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Kaushik Deuti,S. R. Ganesh,Kailash Chandrahm iteration procedure, and the computational steps of the algorithm are detailed. Finally, a pseudo code of CSO algorithm is also presented to demonstrate the implementation of this optimization technique.
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https://doi.org/10.1007/978-981-16-9777-7cluding derivative and other mathematical methods may not be practical to new complex problems. Therefore new optimization algorithms are needed. In the past decades many algorithms were developed and used for different optimization problems, which can be divided into three categories including clas
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Giuseppe Pignatti,Sandro Pignattian educational classroom environment. It has an interesting exclusivity which may facilitate the solution process of optimization problems. In this chapter, a brief literature review of the TLBO algorithm is first presented. Then, the working process and two phases of TLBO (teacher phase and learner
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Biodiversity in the Marine Environmentn model. This algorithm was developed by Gandomi and Alavi (.) and the preliminary studies illustrated its potential in solving numerous complex engineering optimization problems. In this chapter, the natural process behind a standard KHA is described.
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https://doi.org/10.1007/978-3-030-03083-4 sense. In Sect. ., an overview of the implementations of SSO is presented. The underlying idea of the algorithm is discussed in Sect. .. The mathematical formulation and a pseudo-code are presented in Sects. . and ., respectively. Section . is devoted to conclusion.
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Stephen P. Kirkman,Kumbi Kilongo Nsingi, the GE algorithm and the underlying idea are introduced and its applications in some studies are reviewed. Then, the mathematical formulation and a pseudo-code of GE are discussed. Finally, the conclusion is presented.
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