Dimension by dimension dynamic sine cosine algorithm for global optimization problems

Liu, Yu; Zhao, Yiran; Liu, Jingsen

doi:10.1016/j.asoc.2020.106933

Cited by 37 publications

(6 citation statements)

References 58 publications

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“…Based on a greedy per-dimension update strategy, the evolutionary dimension of solutions will not be overlooked due to degradation in other dimensions, allowing any update value that can improve the solution to be accepted. Ensuring that the algorithm can utilize evolutionary information from individual dimensions for better local search, thereby obtaining higher-quality solutions and improving the convergence speed [36].…”

Section: Fusion Lens Imaging Backward Learning and Dimension-by-dimen...mentioning

confidence: 99%

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Ye,

Zhou,

Yang

et al. 2024

Biomimetics

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The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed “Mean Differential Variation”, to enhance the algorithm’s ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.

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Section: Fusion Lens Imaging Backward Learning and Dimension-by-dimen...mentioning

confidence: 99%

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Ye,

Zhou,

Yang

et al. 2024

Biomimetics

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“…The results show that the MSCA can perform well over the original SCA and other population-based algorithms. Li et al (2021a) proposed the dimension-by-dimension dynamic SCA (DDSCA) for global optimization problems. In the DDSCA, dimension by dimension strategy is performed by evaluating the solution in each dimension.…”

Section: Sca Based-learning Strategiesmentioning

confidence: 99%

A comprehensive survey on the sine–cosine optimization algorithm

Rizk‐Allah

Hassanien

2022

Artif Intell Rev

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Metaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine–cosine algorithm (SCA) imitates the behaviour of transcendental functions while the sine and cosine functions are presented to explore and exploit the search space. SCA starts by random population and executes iterative evolution processes to update the standard evolutionary algorithm’s destination or the best location. SCA used linear transition rules to balance the exploration and exploitation searches while searching for the best or optimal solutions. Since Mirjalili proposed it in 2016, SCA has attracted many researchers’ attention to deal with several optimization problems in many fields due to its strengths in solving optimization tasks that include the simple concept, easiness of implementation, and rapid convergence. This paper aims to provide researchers with a relatively comprehensive and extensive overview of the Sine–Cosine optimization algorithm in the literature to inspire further research. It examines the available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, and different applications. Some optimization formulations regarding single-objective optimization problems, multi-objective optimization problems, binary-objective optimization problems, and more classifications regarding the optimization types are discussed. An extensive bibliography is also included.

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“…Şekil 3. KJS kaba kodu (Singh, 2019), DDSCA (Li, Zhao, & Liu, 2021), HGWO (Zhu, Xu, Li, Wu, & Liu, 2015) ve m-SCA (Gupta & Deep, 2019) algoritmalarıyla da karşılaştırılmıştır. Karşılaştırma literatürde sıklıkla kullanılan 30 boyut için yapılmıştır.…”

Section: Araştırma Sonuçları Ve Tartışmaunclassified

Karşıt Tabanlı Öğrenme İle Geliştirilmiş Yapay Denizanası Arama Algoritması

Yildizdan

2022

EJOSAT

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Bu çalışmada denizanalarının okyanustaki yiyecek arama davranışının modellenmesi ile oluşturulan yapay denizanası arama algoritmasının (JS) performansını geliştirmek amacıyla yeni gelişmiş bir algoritma önerilmiştir. Bunun için JS’ye karşıt tabanlı öğrenme yaklaşımı dahil edilerek popülasyondaki bireylerin arama uzayına daha doğru şekilde dağıtılması sağlanmıştır. Geliştirilmiş algoritma(KJS), standart kıyaslama fonksiyonları üzerinde 10,30,50,100,500 ve 1000 boyut için test edilmiştir. Elde edilen sonuçlar JS ve literatürdeki algoritmalarla karşılaştırılmış, istatistik testler ile yorumlanmıştır. Sonuçlar değerlendirildiğinde önerilen KJS algoritmasının başarılı ve kabul edilebilir sonuçlar ürettiği tespit edilmiştir.

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Dimension by dimension dynamic sine cosine algorithm for global optimization problems

Cited by 37 publications

References 58 publications

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

A comprehensive survey on the sine–cosine optimization algorithm

Karşıt Tabanlı Öğrenme İle Geliştirilmiş Yapay Denizanası Arama Algoritması

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