A Modified Crow Search Algorithm with Applications to Power System Problems

Cuevas, Erik; Espejo, Emilio Barocio; Enrı́quez, Arturo Conde

doi:10.1007/978-3-030-11593-7_6

Cited by 9 publications

(6 citation statements)

References 36 publications

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“…It was used by the authors for economical load dispatch. Similar to this, Cuevas et al [26] enhanced CSA by altering two CSA parameters: awareness probabilities and randomized perturbations. They claimed that these changes would damage the population's diversity and accelerate convergence.…”

Section: Related Workmentioning

confidence: 95%

Security-Aware Resource Allocation in Fog Computing Environments

Kumar

2024

Preprint

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Fog computing, an innovative approach building upon cloud technology, taps into local user resources to enhance services. It boasts cost-effectiveness, improved security, and reduced network delays, making it a popular choice for real-time applications. However, due to the heterogeneity of fog devices, resource allocation and scheduling pose challenges. This paper introduces a novel solution using a crow-inspired search mechanism and a multi-objective evolutionary approach for fog computing environments. The primary objectives are to optimize security and success rates simultaneously. A local search method enhances the performance of the crow search algorithm (CSA). The proposed method utilizes evolutionary techniques to allocate and schedule resources effectively. We compared our hybrid CSA with the original CSA and Genetic Algorithm (GA) across seven scenarios with varying parameters. Our hybrid CSA consistently outperformed existing methods, demonstrating its effectiveness in achieving the desired objectives.

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Section: Related Workmentioning

confidence: 95%

Security-Aware Resource Allocation in Fog Computing Environments

Kumar

2024

Preprint

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“…This technique is considered for electromagnetic optimization of the usability factors' hierarchical model in a prediction model [64] as well as for the economic load dispatch problem [65]. Also, a modified crow search algorithm is proposed for solving a power system problems and aims to modify the standard CSA parameters such as the awareness probability and the random perturbation by a dynamic awareness probability (DAP) and a Lévy flights to simulate the evasion behavior of each crow [66]. Furthermore, Huang et al [67] proposed a hybrid CSA algorithm (HCSA) based on the Variable Neighborhood Search (VNS) and the standard SCA method.…”

Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

A Distributed Bi-Behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

et al. 2022

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Dynamic Multi-Objective Optimization Problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization field that have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the standard Crow Search Algorithm has not been considered for either DMOPs or MaOPs to date. This paper proposes a Distributed Bi-behaviors Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function, which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. Two variants of the proposed DB-CSA approach are developed: the first variant is used to solve a set of MaOPs with 2, 3, 5, 7, 8, 10,15 objectives, and the second aims to solve several types of DMOPs with different time-varying Pareto optimal sets and a Pareto optimal front. The second variant of DB-CSA algorithm (DB-CSA-II) is proposed to solve DMOPs, including a dynamic optimization process to effectively detect and react to the dynamic change. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics used to compare the DB-CSA approach to the state-of-the-art MOEAs. The Taguchi method has been used to manage the meta-parameters of the DB-CSA algorithm. All quantitative results are analyzed using the non-parametric Wilcoxon signed rank test with 0.05 significance level, which validated the efficiency of the proposed method for solving 44 test beds (21 DMOPs and 23 MaOPS).

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“…The second on [65] consists in applying a sigmoid transformation and was applied to solve the 2D bin packing problem. Several modified versions of CSA tended to manage the diversity based on the Gaussian distribution and diversity information of the population such in [66] for electromagnetic optimization, the usability factors hierarchical model for feature extraction and prediction [67], the priority-based technique is used to determine the sufficient flight length amount for each crow to update their position based other crow for economic load dispatch problem [68] and the modification of the CSA parameters like; the awareness probability and the random perturbation of each crow is proposed in [69].…”

Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

Aboud¹,

Rokbani²,

Alimi³

et al. 2021

Preprint

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<p>Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed <a>Bi-behaviors </a>Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized. </p>

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A Modified Crow Search Algorithm with Applications to Power System Problems

Cited by 9 publications

References 36 publications

Security-Aware Resource Allocation in Fog Computing Environments

Security-Aware Resource Allocation in Fog Computing Environments

A Distributed Bi-Behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

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