Dynamic crow search algorithm based on adaptive parameters for large-scale global optimization

Necira, Abdelouahab; Naimi, Djemai; Salhi, A.; Salhi, Souhail; Menani, Smail

doi:10.1007/s12065-021-00628-4

Cited by 11 publications

(11 citation statements)

References 53 publications

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“…Coelho et al [26] developed a modified CSA in which Gaussian distribution function is used in order to control two algorithmic parameters. Necira et al [27] proposed an enhanced version of CSA, called dynamic crow search algorithm (DCSA), in which there are two modifications on CSA. Awareness probability is linearly adjusted throughout iterations, and FL is expressed as pareto probability density function.…”

Section: Related Workmentioning

confidence: 99%

Multi Strategy Search with Crow Search Algorithm

Durgut¹,

Aydın²

2022

Optimisation Algorithms and Swarm Intelligence

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Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms developed inspiring of the social behaviour of crow flocks. One of the drawbacks of the original CSA is that it tends to randomly select a neighbour on search strategy due to its low convergence rate, which pushes the search to stick in local optima due to the same search strategy applied across iterations. The multi-strategy search for CSA (CSA-MSS) has been proposed to enrich the search facilities and provide diversity to overcome these drawbacks. The multi-strategy search implies utilising a pool of strategies consists of six different types of search operators. The multi-strategy approach with a selection mechanism has not been proposed for CSA before and implemented first time. The comparative performance analysis for the proposed algorithm has been conducted over solving 24 benchmark problems. The results demonstrate that the proposed approach is outperforming well-known state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Multi Strategy Search with Crow Search Algorithm

Durgut¹,

Aydın²

2022

Optimisation Algorithms and Swarm Intelligence

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“…CSA has the advantages of easy to understand and implement, few control parameters, good universality and strong global search ability. Since its development, CSA has been widely used in many fields, including numerical optimization (Khalilpourazari and Pasandideh 2020 ; Necira et al 2022 ; Gholami et al 2021 ), feature selection (Sayed et al 2019 ; Ouadfel and Abd Elaziz 2020 ), image processing (Upadhyay and Chhabra 2020 ; Fred et al 2020 ), optimal power flow (Saha et al 2017 ; Fathy and Abdelaziz 2018 ), economic load dispatch (Mohammadi and Abdi 2018 ; Spea 2020 ), cloud computing (Kumar and Vimala 2019 ; Kumar and Kousalya 2020 ), control engineering (Turgut et al 2020 ), and chemical engineering (Abdallh and Algamal 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Later in Makhdoomi and Askarzadeh ( 2020 ), an adaptive chaotic awareness probability AP was formulated to improve CSA’s efficiency. Also, Necira et al ( 2022 ) designed a dynamic CSA (DCSA) with dynamic fl changes based on the generalized Pareto probability density function, and AP adjusted linearly over optimization process. Search mode improvement strategies.…”

Section: Introductionmentioning

confidence: 99%

Enhanced crow search algorithm with multi-stage search integration for global optimization problems

Peng

Zhang

et al. 2023

Soft Comput

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Crow search algorithm (CSA), as a new swarm intelligence algorithm that simulates the crows’ behaviors of hiding and tracking food in nature, performs well in solving many optimization problems. However, while handling complex and high-dimensional global optimization problems, CSA is apt to fall into evolutionary stagnation and has slow convergence speed, low accuracy, and weak robustness. This is mainly because it only utilizes a single search stage, where position updating relies on random following among individuals or arbitrary flight of individuals. To address these deficiencies, a CSA with multi-stage search integration (MSCSA) is presented. Chaos and multiple opposition-based learning techniques are first introduced to improve original population quality and ergodicity. The free foraging stage based on normal random distribution and Lévy flight is designed to conduct local search for enhancing the solution accuracy. And the following stage using mixed guiding individuals is presented to perform global search for expanding the search space through tracing each other among individuals. Finally, the large-scale migration stage based on the best individual and mixed guiding individuals concentrates on increasing the population diversity and helping the population jump out of local optima by moving the population to a promising area. All of these strategies form multi-level and multi-granularity balances between global exploration and local exploitation throughout the evolution. The proposed MSCSA is compared with a range of other algorithms, including original CSA, three outstanding variants of CSA, two classical meta-heuristics, and six state-of-the-art meta-heuristics covering different categories. The experiments are conducted based on the complex and high-dimensional benchmark functions CEC 2017 and CEC 2010, respectively. The experimental and statistical results demonstrate that MSCSA is competitive for tackling large-scale complicated problems, and is significantly superior to the competitors.

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“…Necira et al . ( Necira, Naimi, Salhi, Salhi, & Menani, 2021 ) proposed a dynamic CSA (DCSA) based on linearly adjusted perceptual probability and flight length adjusted by generalized Pareto probability density function to address the ease of falling into local optimum defect caused by fixed flight length and perceptual probability in CSA.…”

Section: Introductionmentioning

confidence: 99%