A probabilistic simplified sine cosine crow search algorithm for global optimization problems

Rao, Y. Srinivasa; He, Dengxu; Qu, Liangdong

doi:10.1007/s00366-021-01578-2

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Recently, there have been a lot of scholars who prefer metaheuristics due to their simplicity and high efficiency in solving problems. Many metaheuristics are of great importance in several areas [1][2][3]. According to Fister et al [4], metaheuristics can be classified into four types: swarm intelligence, evolutionary, physics-based, and other.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection

Zheng¹,

Chen²

2023

Computer Systems Science and Engineering

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To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At last, we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals. Therefore, the application of ISMO is validated by ten benchmark functions and feature selection. It is proved that the proposed method can resolve the problem of feature selection.

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Section: Introductionmentioning

confidence: 99%

“…2 max and λ 2 = t 2 /T 2 max are dynamic parameters that adjust adaptively with the number of iterations.The steps of ISMO are illustrated in Algorithm 1. Location update for all the spider monkeys based on LLP with Eq.…”

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confidence: 99%