Titlebook: Nature-Inspired Optimizers; Theories, Literature Seyedali Mirjalili,Jin Song Dong,Andrew Lewis Book 2020 Springer Nature Switzerland AG 202

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书目名称Nature-Inspired Optimizers被引频次学科排名




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书目名称Nature-Inspired Optimizers年度引用学科排名




书目名称Nature-Inspired Optimizers读者反馈




书目名称Nature-Inspired Optimizers读者反馈学科排名

BLA

Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection,A is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm i

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FILTH

Grey Wolf Optimizer: Theory, Literature Review, and Application in Computational Fluid Dynamics Proeveral experiments are conducted to analyze and benchmark the performance of different variants and improvements of this algorithm. The chapter also investigates the application of the GWO variants in finding an optimal design for a ship propeller.

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考博

,Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering,This chapter discusses the theoretical foundation, operations, and main strengths behind this algorithm. Moreover, a detailed literature review is conducted to discuss several variants of the MVO algorithm. In addition, the main applications of MVO are also thoroughly described. The chapter also inv

CRUMB

Moth-Flame Optimization Algorithm: Theory, Literature Review, and Application in Optimal Nonlinear nite dimensional. In order to convert it to a finite dimensional optimization problem, a collocation type method is proposed. The collocation approach is based on approximating the control input function as a series of given base functions with unknown coefficients. Then, the optimal control problem

不可磨灭

famine

Salp Swarm Algorithm: Theory, Literature Review, and Application in Extreme Learning Machines,ous applications since its proposal. In this chapter, the algorithm, its operators, and some of the remarkable works that utilized this algorithm are presented. Moreover, the application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the

故意

Sine Cosine Algorithm: Theory, Literature Review, and Application in Designing Bend Photonic Crystaf optimization problems. After discussing the mathematical model, a brief literature review is given covering the most recent improvements and applications of this algorithm. The performance of this algorithm is benchmarked on a wide range of test functions showing the flexibility of SCA in solving