Engineering optimisation by cuckoo search

Yang, Xin-She; Deb, Suash

doi:10.1504/ijmmno.2010.035430

Cited by 1,468 publications

(1,042 citation statements)

References 21 publications

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“…The ultimate goal is to develop systems that have ability to learn incrementally, to be adaptable to their environment and to be tolerant to noise. One of the recent developed bioinspired algorithms is the Cuckoo Search (CS) [16] which is based on style life of Cuckoo bird. Cuckoos use an aggressive strategy of reproduction that involves the female hack nests of other birds to lay their eggs fertilized.…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

“…Sometimes, the egg of cuckoo in the nest is discovered and the hacked birds discard or abandon the nest and start their own brood elsewhere. The Cuckoo Search proposed by Yang and Deb 2009 [16] is based on the following three idealized rules:…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

“…Lévy flights essentially provide a random walk while their random steps are drawn from a Lévy distribution for large steps which has an infinite variance with an infinite mean (eq.8). Here the consecutive jumps/steps of a cuckoo essentially form a random walk process which obeys a power-law steplength distribution with a heavy tail [16]. (7) (8) where α > 0 is the step size which should be related to the scales of the problem of interest.…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

“…One of the most recent variant of PSO algorithm is Cuckoo Search algorithm (CS). CS is an optimization algorithm developed by Xin-She Yang and Suash Deb in 2009 [16]. It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds (of other species).…”

Section: Introductionmentioning

confidence: 99%

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A novel quantum inspired cuckoo search for knapsack problems

Layeb

2011

IJBIC

162

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Abstract-The Bin Packing Problem (BPP) is one of the most known combinatorial optimization problems. This problem consists to pack a set of items into a minimum number of bins. There are several variants of this problem; the most basic problem is the onedimensional bin packing problem (1-BPP). In this paper, we present a new approach based on the quantum inspired cuckoo search algorithm to deal with the 1-BPP problem. The contribution consists in defining an appropriate quantum representation based on qubit representation to represent bin packing solutions. The second contribution is proposition of a new hybrid quantum measure operation which uses first fit heuristic to pack no filled objects by the standard measure operation. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed approach.

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Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel quantum inspired cuckoo search for knapsack problems

Layeb

2011

IJBIC

162

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“…Yang et al tuned the controller parameters using cuckoo search algorithm [29]. Kiam et al modelled a PID control system and tuned the parameters of the control system using intelligent control techniques [14].…”

Section: Introductionmentioning

confidence: 99%

Tuning of Robust PID Controller with Filter for SISO System Using Evolutionary Algorithms

Muniraj¹,

Siluvairaj²,

Ramaveerapathiran³

2017

STUD INFORM CONTROL

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This paper discusses the performance comparison of Evolutionary Algorithm techniques with a view to tuning the desired design parameters of robust PID controller with filter. The design parameters of robust PID controller such as proportional gain K P , integral time constant T i , derivative time constant T d and filter time constant t d /N for Single Input Single Output (SISO) system are computed using Real coded Genetic Algorithm (RGA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm. The design parameters are optimized using the statistical measures in twenty independent simulation runs. The computed design parameters obtained using Evolutionary Algorithms are used to determine the performance specifications of the Robust PID controller. The performance specifications determined are robustness with respect to model uncertainties and disturbance attenuation, set point tracking, load disturbance rejection and control energy. The test systems used to ascertain the performance specifications are Phase Locked Loop (PLL) system with motor control and Magnetic Levitation system (MLS). For PLL system, the output response obtained using the computed design parameters by RGA algorithm has proved to be better than DE and PSO. For MLS system, the output response obtained using the computed design parameters by DE algorithm has been better than PSO and RGA. meet out the performance specifications of the given system mainly depends on the tuning of the controller .Since the system has nonlinear plant and various uncertainties the tuning of the robust PID controller becomes difficult. This leads to poor tuning. The reasons of the poor tuning is due to a) lack of knowledge among operators and commissioning personnel b) variety of PID structures that leads to error during tuning c) in some cases the nature of tuning method do not meet the requirement of the process involved [3]. Out of the installed PID controllers in industry, nearly one third controllers are tuned manually and remaining two third are tuned automatically. But the automatically tuned PID controllers are not effective [7].To overcome these limitations optimization algorithms are used for the tuning of robust PID controller. In the past many researchers have used different optimization algorithm for tuning the parameters of robust PID controller. But the parameter tuned to meet out the performance specifications of the system is different in each case. Many researchers have considered set In this paper, Evolutionary algorithm is used to determine the optimal parameters for the robust optimal controller. Evolutionary Algorithms used are Particle Swarm Optimization (PSO), Real coded Genetic Algorithm (RGA) and Differential Evolution (DE). The main focus of this paper is to determine the design parameters of robust PID controller using evolutionary algorithm such that it meet out the system performance specifications like robustness with respect to model uncertainties and disturbance attenuation, set point tracking,...

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Optimal size of photovoltaic pumping system using nature‐inspired algorithms

Bakelli

Kaabeche

2019

Int Trans Electr Energ Syst

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Summary In this article, an optimal design model using metaheuristic techniques to overcome such difficult optimization problems has been proposed. For this purpose, various well‐known metaheuristic algorithms have been used, namely: ant lion optimizer (ALO), bat algorithm (BA), cuckoo search algorithm (CSA), and gray wolf optimization algorithm (GWO). To provide optimal system configurations, an objective function based on the total net present cost (TNPC) has been specified while taking into account a number of constraints, including the loss of power supply probability (LPSP). The effectiveness of the used algorithms in solving such problem is assessed and their performances compared with each other. To assess the suggested model's effectiveness, a case study has been conducted to investigate a stand‐alone photovoltaic water pumping system (PVWPS), designed to supply water to a one‐hectare field of date palm trees. This field of an oasis located in the Ghardaïa region of Algeria consists of 130 date palm trees spaced 9 m apart. The simulation results show the effectiveness of the GWO algorithm over the other algorithms. GWO converges toward the optimal solutions for different heads and all reliability levels required by the customer. Another aspect, supported by the developed methodology, is the accounting of the excess water volume. It has been found that the relative surplus is inversely proportional to the PVWPS reliability level. For a head of 30 m, the relative excess is estimated at 120% for LPS0%, while it is 27% for LPSP of 10%. The same observations are made for the other heads.

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Engineering optimisation by cuckoo search

Cited by 1,468 publications

References 21 publications

A novel quantum inspired cuckoo search for knapsack problems

A novel quantum inspired cuckoo search for knapsack problems

Tuning of Robust PID Controller with Filter for SISO System Using Evolutionary Algorithms

Optimal size of photovoltaic pumping system using nature‐inspired algorithms

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