Cuckoo Optimization Algorithm

Rajabioun, Ramin

doi:10.1016/j.asoc.2011.05.008

Cited by 1,014 publications

(497 citation statements)

References 14 publications

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“…COA has been tested for five benchmark functions and found to converge faster than GA and PSO algorithms [29]. The main reason that makes COA work better than other nature inspired computing algorithms lies in multiple functions of COA operators (including egg laying and immigration operators).…”

Section: Cuckoo Optimization Algorithmmentioning

confidence: 99%

“…COA is a population-based evolutionary algorithm recently developed by Rajabioun in 2011 [29]. COA is inspired by the brood parasitism of some cuckoo species.…”

Section: Cuckoo Optimization Algorithmmentioning

confidence: 99%

“…It has not taken into account the immigration behavior of Cuckoos. Rajabioun developed another algorithm based on cuckoo lifestyle, called "Cuckoo Optimization Algorithm" (COA) [29]. This algorithm inspires and models cuckoo life cycle much better and more precisely.…”

Section: Introductionmentioning

confidence: 99%

“…COA is a population-based technique introduced in [29]. The COA is one of the several recent and powerful metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

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Design of Linear and Circular Antenna Arrays Using Cuckoo Optimization Algorithm

Singh¹,

Rattan²

2014

PIER C

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Abstract-Cuckoo optimization Algorithm (COA) is employed for the optimization of linear and nonuniform circular antenna arrays. COA is a novel nature inspired computing algorithm which is motivated by the life of Cuckoo. Like other nature-inspired algorithms, COA is also a population-based method and uses a population of solutions to proceed to the global solution. The method of COA is used to determine a set of parameters of antenna elements that provide the required radiation pattern. The effectiveness of COA for the design of antenna arrays is shown by means of numerical results.Comparison of results of COA is made with that obtained using other popular methods. The results reveal the superior performance of COA as compared to other techniques both for design of linear and circular antenna arrays.

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Section: Cuckoo Optimization Algorithmmentioning

confidence: 99%

“…COA is a population-based evolutionary algorithm recently developed by Rajabioun in 2011 [29]. COA is inspired by the brood parasitism of some cuckoo species.…”

Section: Cuckoo Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…COA is a population-based technique introduced in [29]. The COA is one of the several recent and powerful metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of Linear and Circular Antenna Arrays Using Cuckoo Optimization Algorithm

Singh¹,

Rattan²

2014

PIER C

View full text Add to dashboard Cite

show abstract

“…GA finds the right solution quickly, after about 500 trials, but it has a problem with improving them afterwards. The fourth method of optimization is a metaheuristic known for several years which is called COA (Rajabioun 2011). The method is interesting, yet it takes into account many parameters which affect it's effectiveness.…”

Section: Comparison Between the Proposed Cripso And Other Algorithmsmentioning

confidence: 99%

A metaheuristic for fast machining error compensation

Stryczek

2014

J Intell Manuf

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In case of complex parts machining or multidirectional machining in multi-part fixtures the error compensation in multi-dimensional decision space poses a difficult problem. The article focuses on the limitation of defective products by means of systematic increase of the remaining error budget due to correction of the setup data. A vectorial equation for machine tool space description is presented. The development of geometric dimensioning and tolerancing scheme to the levels connected with the setup data is proposed. The optimization algorithm used here is based on the paradigm particle swarm optimization (PSO), but it includes a few significant modifications inspired by the growth of the coral reef thus the name of the method-coral reefs inspired particle swarm optimization (CRIPSO). CRIPSO has been compared with three other popular metaheuristics: classic PSO, genetic algorithm, and cuckoo optimization algorithm. There is a practical example in this article.

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A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study

Wang

Wang²,

Zhang³

et al. 2021

Int Trans Electr Energ Syst

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Summary The present paper describes a new optimal design for an autonomous renewable energy‐based CHP system for a remote area in Zhidoi county, China. The configuration contains different parts of the electric heater (EH), photovoltaic‐thermal (PV/T), wind turbines (WTs), thermal energy storage (TES), and electrical energy storage (EES). The total annual cost (TAC) is utilized as a cost function of the system configuration and the idea is to minimize this function to access an optimal configuration. Due to the complicated nonlinear nature of this system, a metaheuristic‐based method, called Improved Marine Predators Algorithm (IMPA) has been introduced and designed. The reason for using this new algorithm is to cover the main drawbacks of most metaheuristics like better accuracy and higher convergence speed. To show the capability of the designed IMPA, it is validated by some different new metaheuristics from the literature. Afterward, the algorithm is used for system configuration optimization. Some sensitivity analysis is also investigated to shoe the method capability and the final results confirm the high ability of the proposed method for providing an optimal renewable energy‐based CHP system. The final results show a $56 307.74 value of TAC. The total efficiency of the system for the winter and the summers are 66% and 61%, respectively and the minimum total annual cost happens at AD = 0.8983. Finally, the minimum value of the total annual cost is achieved by the proposed method with 922.4 kWh.

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Cuckoo Optimization Algorithm

Cited by 1,014 publications

References 14 publications

Design of Linear and Circular Antenna Arrays Using Cuckoo Optimization Algorithm

Design of Linear and Circular Antenna Arrays Using Cuckoo Optimization Algorithm

A metaheuristic for fast machining error compensation

A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study

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