An improved Crow Search Algorithm for high-dimensional problems

Jain, Mamta; Rani, Asha; Singh, Vijander

doi:10.3233/jifs-17275

Cited by 47 publications

(25 citation statements)

References 22 publications

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“…To test the efficiency of the proposed improved algorithm, we compared ISCSA with WOA [11], PSO [12],GWO [13], SSA [14], HHO [15], GSA [17], CSA [22] and ICSA [27], BOA [52]. To ensure the algorithm was useful, these algorithms have been executed in the MATLAB R2017a computing environment on a computer with an i5-4210H 2.9 GHz processor with 4GB RAM (Random Access Memory).…”

Section: B Parameter Settings and Comparisons Of Operating Results Tmentioning

confidence: 99%

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An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

Han¹,

Xu²,

Yue³

et al. 2020

IEEE Access

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The crow search algorithm (CSA) is a new intelligent optimization algorithm based on the behavior of the crow population, which has been proven to perform well. However, its simple search mechanism also leads to the algorithm's slow convergence speed and its ease of falling into local optimization when solving complex optimization problems. In order to overcome these problems, this paper proposes an improved CSA (ISCSA) based on a spiral search mechanism. By introducing a weight coefficient, an optimal guidance position and a spiral search mechanism, the position equation was updated to accelerate the convergence of the algorithm and make the exploration and exploitation of CSA more balanced. Meanwhile, adding Gaussian variation and random perturbation strategy made it difficult for the algorithm to fall into local optimization. The advantages of the proposed ISCSA were evaluated using 23 benchmark functions and four classical engineering design problems. The experimental and statistical results of 23 test functions showed that the proposed ISCSA could escape from the local optima with higher accuracy and faster convergence than both the CSA and other meta-heuristic optimization algorithms. The calculation results of the four engineering optimization problems showed that compared with other algorithms, ISCSA can solve the practical optimization problem well and has been proved to have strong competitiveness and good performance. INDEX TERMS Crow search algorithm, weight coefficient, spiral search mechanism, engineering design optimization.

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Section: B Parameter Settings and Comparisons Of Operating Results Tmentioning

confidence: 99%

“…The crow search algorithm (CSA) is a recently proposed evolutionary algorithm based on crow foraging behavior [22]. The main idea is that when a crow looks for its own hidden food, the other crows will follow and steal the food with a certain probability [22], [24]- [27].…”

Section: Crow Search Algorithmmentioning

confidence: 99%

An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

Han¹,

Xu²,

Yue³

et al. 2020

IEEE Access

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“…Optimization [10], [11], Artificial Bee Colony [12], Ant Colony [13], Crow Search Optimization [14], Cat Swarm Optimization [15], Grey Wolf Optimization [16], Whale Optimization [17]. The advantages/disadvantages of these methods are all well documented in literature.…”

Section: Other Methods Available Include Particle Swarmmentioning

confidence: 99%

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Jiménez-Gutiérrez¹,

Sánchez‐Partida²,

Martínez‐Flores³

et al. 2020

Adv. sci. technol. eng. syst. j.

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A distribution company in Mexico covers the travel expenses for 21 sales representatives. Currently, the routes they follow are not established clearly, which can lead to high costs in this subject. A reduction of such cost is sought after, by optimizing the routes for each one of them. The following research finds an improvement on the routes for the sales representative of a distribution company in Mexico. It was done by using the Traveling Salesman Problem with Hotel Selection or Base selections via a Simulated Annealing algorithm. The results show an improvement in a reasonable timeframe by using the Simulated Annealing. It also shows that the maximum process time was of 156.63 minutes, and the least amount of improvement was 24.44% over the current route selection. Applying this model will be beneficial for the company as the company is trying to reduce costs related to the sales representatives such as; fuel cost, hotel cost, and travel expenses.

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“…Another effort to enhance the original CSA to be able to solve high dimensional optimization tasks is shown in [108] where three operators have been added to a balance between exploration and exploitation. These factors are Lévy flight, experience factor, and adaptive adjustment factor.…”

Section: ) Improved Csamentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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An improved Crow Search Algorithm for high-dimensional problems

Cited by 47 publications

References 22 publications

An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Crow Search Algorithm: Theory, Recent Advances, and Applications

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