Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm

Chiroma, Haruna; Herawan, Tutut; Fister, Iztok; Kareem, Sameem Abdul; Shuib, Liyana; Hamza, Mukhtar Fatihu; Saadi, Younes; Abubakar, Adamu

doi:10.1016/j.asoc.2017.07.053

Cited by 62 publications

(29 citation statements)

References 68 publications

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“…In Table 1, each row changes p anewLowerValue and p anewU pperValue , and each column changes p anewLower and p anewU pper . This paper selects α = 1 and p a = 0.25, which are mentioned by Yang and Deb [18] and recent literature [28]. With functions of Table 2, we did 9600 experiments, and the results are shown in Figure 1c.…”

Section: Step Size and P A Are Changed By Coefficient Functionmentioning

confidence: 99%

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Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map

Liu

Wang

et al. 2018

Algorithms

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Cuckoo Search (CS) is a Meta-heuristic method, which exhibits several advantages such as easier to application and fewer tuning parameters. However, it has proven to very easily fall into local optimal solutions and has a slow rate of convergence. Therefore, we propose Modified cuckoo search algorithm with variational parameter and logistic map (VLCS) to ameliorate these defects. To balance the exploitation and exploration of the VLCS algorithm, we not only use the coefficient function to change step size α and probability of detection p a at next generation, but also use logistic map of each dimension to initialize host nest location and update the location of host nest beyond the boundary. With fifteen benchmark functions, the simulations demonstrate that the VLCS algorithm can over come the disadvantages of the CS algorithm.In addition, the VLCS algorithm is good at dealing with high dimension problems and low dimension problems.

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Section: Step Size and P A Are Changed By Coefficient Functionmentioning

confidence: 99%

“…Chiroma et al [28] found that the population reduction and usage of biased random walk are the most frequently used modifications. To improve the CS algorithm, this paper chooses a different method.…”

Section: Introductionmentioning

confidence: 99%

Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map

Liu

Wang

et al. 2018

Algorithms

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“…Some studies are undertaken in the improvement of traditional metaheuristic algorithms [8][9][10]. In [8], the MapReduce programming paradigm is implemented in 2 Mathematical Problems in Engineering the Apache Spark tool.…”

Section: Introductionmentioning

confidence: 99%

“…The Cuckoo Search binary algorithm is proposed and applied to different instances of the crew scheduling problem. In [9], the modifications of the original CS algorithms are summarized. The continuous, binary, and multiobjective CS algorithms are compared both in the aspects of benchmark tests and in the applications.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications

Ding

Wang

Zhang

et al. 2019

Mathematical Problems in Engineering

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This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.

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“…Generally, the modification can be categorized into two classes. One is the adjustment of the parameters, and the other is hybridization with other intelligence algorithms [32]. For the first method, Uroš Mlakar et al [33] proposed a hybrid self-adaptive CS algorithm (HSA-CS) with which the search space was explored using three exploration search strategies, i.e., random long-distance search strategy, stochastic moderate-distance search strategy and stochastic short-distance search strategy.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

et al. 2018

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The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.

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Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm

Cited by 62 publications

References 68 publications

Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map

Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map

A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications

A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

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