A Cuckoo Search Algorithm with Complex Local Search Method for Solving Engineering Structural Optimization Problem

Discrete rational approximation models to the non-integer order differentiator sλ, where λ ε (0, 1), using Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The proposed metaheuristic optimization approach used to design the discrete non-integer order differentiators (DNODs) does not employ any s-to-z domain mapping function to perform the discretization operation. Frequency domain characteristics of DNODs, solution reliability, and algorithm convergence performances are investigated among MFO and an advanced evolutionary algorithm called Particle Swarm Optimization with adaptive inertia weight (PSO-w). Results demonstrate the effectiveness of MFO in outperforming PSO-w in solving this non-linear and multimodal optimization problem. The proposed DNODs also exhibit better performance in comparison with the designs based on techniques such as Nelder-Mead Simplex algorithm and Cuckoo Search Algorithm published in recent literature.

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Cuckoo search algorithm based on frog leaping local search and chaos theory

Applied Mathematics and Computation ◽

10.1016/j.amc.2015.06.041 ◽

2015 ◽

Vol 266 ◽

pp. 1083-1092 ◽

Cited By ~ 14

Author(s):

Xueying Liu ◽

Meiling Fu

Keyword(s):

Local Search ◽

Chaos Theory ◽

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm

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An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

Computational Intelligence and Neuroscience ◽

10.1155/2014/970456 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 14

Author(s):

Yanhong Feng ◽

Ke Jia ◽

Yichao He

Keyword(s):

Optimization Problem ◽

Feasible Solution ◽

Search Algorithm ◽

Cuckoo Search ◽

Genetic Mutation ◽

Cuckoo Search Algorithm ◽

Knapsack Problems ◽

Local Optimum ◽

Transform Method ◽

Continuous Space

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.

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Evaluation of Fast Evolutionary Programming, Firefly Algorithm and Mutate-Cuckoo Search Algorithm In Single-Objective Optimization

International Journal of Electrical & Electronic Systems Research (IEESR) ◽

10.24191/ieesr.v9i1.1038 ◽

2019 ◽

Vol 9 (1) ◽

pp. 1

Author(s):

Muhammad Zakyizzuddin Bin Rosselan ◽

Shahril Irwan Bin Sulaiman ◽

Norhalida Othman

Keyword(s):

Evolutionary Algorithm ◽

Firefly Algorithm ◽

Optimization Problem ◽

Search Algorithm ◽

Cuckoo Search ◽

Evolutionary Programming ◽

Cuckoo Search Algorithm ◽

Computational Time ◽

Fitness Value ◽

Single Objective

In this study proposes an evaluation of different computational intelligences, i.e Fast-Evolutionary Algorithm (FEP), Firefly Algorithm (FA) and Mutate-Cuckoo Search Algorithm (MCSA) for solving single-objective optimization problem. FEP and MCSA are based on the conventional Evolutionary Programming (EP) and Cuckoo Search Algorithm (CSA) with modifications and adjustment to boost up their search ability. In this paper, four different benchmark functions were used to compare the optimization performance of these three algorithms. The results showed that MCSA is better compare with FEP and FA in term of fitness value while FEP is fastest algorithm in term of computational time compare with other two algorithms.

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Optimization of the operation of Dokan hydropower development using Cuckoo Search Algorithm

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1216/1/012016 ◽

2022 ◽

Vol 1216 (1) ◽

pp. 012016

Author(s):

K Ahmad-Rashid

Keyword(s):

Optimization Problem ◽

Initial Level ◽

Search Algorithm ◽

Energy Generation ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Metaheuristic Algorithms ◽

Hydropower Plant ◽

Hydropower Development

Abstract In this paper one of the recently developed metaheuristic algorithms, the Cuckoo Search algorithm is used for the optimization of the operation of a large hydropower plant in Kurdistan, Iraq. The optimization problem is to realize an annual planned energy generation with monthly imposed fractions. The obtained results are excellent, nevertheless, there are some limitations of the algorithm determined by the initial level into the reservoir and a certain correlation between the type of the year, the starting level and the planned energy to be realized.

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