An Enhanced Cuckoo Search Algorithm for Solving Optimization Problems

Kamoona, Ammar Mansoor; Patra, J.C.; Stojcevski, Alex

doi:10.1109/cec.2018.8477784

Cited by 21 publications

(12 citation statements)

References 24 publications

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“…The best nests with a high quality of eggs (solutions) carry over to the next generations, where the quality evaluation is based on the fitness function F of the habitat (array of 1 × D ). The host can discover an alien egg with probability P a from [0, 1], which is approximated by a fraction P a of the N nests being replaced by new nests, having new random solutions [24].…”

Section: Quadrotor Identification Using Intelligent Methodsmentioning

confidence: 99%

Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach

Gmili

Mjahed

Kari

et al. 2019

Computational Intelligence and Neuroscience

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This paper explores the model parameters estimation of a quadrotor UAV by exploiting the cooperative particle swarm optimization-cuckoo search (PSO-CS). The PSO-CS regulates the convergence velocity benefiting from the capabilities of social thinking and local search in PSO and CS. To evaluate the efficiency of the proposed methods, it is regarded as important to apply these approaches for identifying the autonomous complex and nonlinear dynamics of the quadrotor. After defining the quadrotor dynamic modelling using Newton–Euler formalism, the quadrotor model’s parameters are extracted by using intelligent PSO, CS, PSO-CS, and the statistical least squares (LS) methods. Finally, simulation results prove that PSO and PSO-CS are more efficient in optimal tuning of parameters values for the quadrotor identification.

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Section: Quadrotor Identification Using Intelligent Methodsmentioning

confidence: 99%

Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach

Gmili

Mjahed

Kari

et al. 2019

Computational Intelligence and Neuroscience

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“…CSO was developed by Xin-She Yang and Suash Deb in 2009 by transforming the parasitic life situation resulting from some cuckoo birds laying their eggs in their nests to other birds' nests into an optimization technique [21]. Some cuckoo species can mimic the shape and color of the nesting bird, which increases the likelihood of breeding.…”

Section: Cuckoo Search Optimization (Cso)mentioning

confidence: 99%

Comparison of Maximum Power Point Tracking Methods Using Metaheuristic Optimization Algorithms for Photovoltaic Systems

BİLGİN

Yazıcı

2021

Sakarya University Journal of Science

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The maximum power value that can be obtained from photovoltaic systems can change continuously due to environmental conditions such as temperature, sunlight and partial shading. Direct current-direct current (DC-DC) converters and maximum power point tracking (MPPT) algorithms are required, especially in cases of partial shading, in order for the photovoltaic systems to operate at the maximum power point, that is, to draw the maximum possible power value from the system. In this study, simulation studies has been carried out for two different partially shaded scenarios using the boost-type DC-DC converter and MPPT algorithm in the PV array consisting of 3 panels connected in series. In the simulation studies, the output powers obtained by the application of particle swarm optimization, cuckoo optimization, bat optimization and firefly optimization techniques as MPPT algorithm has been compared. In the scenarios examined, the firefly optimization algorithm reached the maximum power point faster, and it has been observed that the firefly optimization method obtained the highest average power at the end of the simulation periods.

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“…First a comparison between the original EPC, first modified EPC (MEPC1), and second modified EPC (MEPC2) are provided. Another comparison is conducted between MEPC1 and MEPC2 against Genetic Algorithm (GA) [7], Imperialist Competitive Algorithm (ICA) [9], Particle Swarm Optimization (PSO) [10], Artificial Bee Colony (ABC) [26], Differential Equation (DE) [8], Harmony Search (HS) [14], Invasive Weed Optimization (IWO) [27], Grey Wolf Optimizer (GWO) [28], and Cuckoo search (CS) [29]. The last comparison aims to investigate the performance of the two proposed modified algorithms against other algorithms available in the literature.…”

Section: Comparison Between Algorithmsmentioning

confidence: 99%

Two-Modified Emperor Penguins Colony Optimization Algorithms

Wahdan¹,

Abdelslam²,

Abou-El-Enien³

et al. 2020

RIA

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Meta heuristic algorithms are very important methods, they used mainly in solving combinatorial optimization problems. They are stochastic in manner and simulate the behavior of any population of particles. Meta heuristics algorithms try to find optimal or near to optimal solution when solving complex optimization problem. In this paper two modified emperor penguins colony optimization algorithms MEPC1 and MEPC2 were developed. Original Emperor Penguins Colony (EPC) algorithm simulates the behavior of emperor of penguins, two modifications are done to the original EPC, Archimedes and hyperbolic spiral like movement are used instead of logarithmic spiral like movement. The two modified algorithms are compared with the original algorithm through ten test functions; the results show that the modified algorithms are achieved better than the original one.

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An Enhanced Cuckoo Search Algorithm for Solving Optimization Problems

Cited by 21 publications

References 24 publications

Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach

Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach

Comparison of Maximum Power Point Tracking Methods Using Metaheuristic Optimization Algorithms for Photovoltaic Systems

Two-Modified Emperor Penguins Colony Optimization Algorithms

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