A hybridization of cuckoo search and particle swarm optimization for solving optimization problems

Chi, Rui; Su, Yixin; Zhang, Danhong; Chi, Xuexin; Zhang, Huajun

doi:10.1007/s00521-017-3012-x

Cited by 49 publications

(18 citation statements)

References 43 publications

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“…From the formulation of CSDE to its implementation and comparison, it can be clearly seen that CSDE is a promising algorithm. The proposed CSDE has been compared with other metaheuristic algorithms including CS [20], DE [23], PSO [37], BA [38], HS [39], ICS [40], CSPSO [41], and IA [42]. The comparison results show that the proposed CSDE algorithm is more superior to other algorithms for most of optimized problems.…”

Section: Discussionmentioning

confidence: 99%

“…For the purpose of verifying the performance of CSDE, we compare its performance on ten benchmark functions (in Table 1) with six other optimization approaches, which are CS [20], DE [23], PSO [37], BA [38], HS [39], ICS [40], and CSPSO [41].…”

Section: Simulation and Comparisonmentioning

confidence: 99%

“…The coordinates and capacity of each demand point are given in Table 5. The simulation results are compared with six different algorithms (CS [20], PSO [37], BA [38], ICS [40], CSPSO [41], and IA [42]). The parameters of these considering algorithms for the problem are set as the same as those described in Section 4.2.…”

Section: Application Studies On the Logistics Distribution Center Locmentioning

confidence: 99%

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A Hybridization of Cuckoo Search and Differential Evolution for the Logistics Distribution Center Location Problem

Chi

et al. 2019

Mathematical Problems in Engineering

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The location selection of logistics distribution centers is a crucial issue in the modern urban logistics system. In order to achieve a more reasonable solution, an effective optimization algorithm is indispensable. In this paper, a new hybrid optimization algorithm named cuckoo search-differential evolution (CSDE) is proposed for logistics distribution center location problem. Differential evolution (DE) is incorporated into cuckoo search (CS) to improve the local searching ability of the algorithm. The CSDE evolves with a coevolutionary mechanism, which combines the Lévy flight of CS with the mutation operation of DE to generate solutions. In addition, the mutation operation of DE is modified dynamically. The mutation operation of DE varies under different searching stages. The proposed CSDE algorithm is tested on 10 benchmarking functions and applied in solving a logistics distribution center location problem. The performance of the CSDE is compared with several metaheuristic algorithms via the best solution, mean solution, and convergence speed. Experimental results show that CSDE performs better than or equal to CS, ICS, and some other metaheuristic algorithms, which reveals that the proposed CSDE is an effective and competitive algorithm for solving the logistics distribution center location problem.

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Section: Discussionmentioning

confidence: 99%

Section: Simulation and Comparisonmentioning

confidence: 99%

Section: Application Studies On the Logistics Distribution Center Locmentioning

confidence: 99%

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A Hybridization of Cuckoo Search and Differential Evolution for the Logistics Distribution Center Location Problem

Chi

et al. 2019

Mathematical Problems in Engineering

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“…In the case of the cuckoo search, hybrid versions of this algorithm have been applied in [62] to numerical optimization problems in engineering. In [63] cuckoo search was applied to benchmark functions and engineering design problems. An algorithm with reinforced learning was used in [64] to solve a logistic distribution problem.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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“…The search evaluated the so-called CS Algorithm with Hill Climbing (CSAHC) on global optimization tasks and found CSAHC effective modification. Another hybrid variant of CS was proposed in [16] where CS was hybridized with PSO for solving continuous optimization and engineering design problems. The research proposed a modification in population initialization, adaptively adjusting control parameters and the incorporation of PSO.…”

Section: Introductionmentioning

confidence: 99%

Personal Best Cuckoo Search Algorithm for Global Optimization

Hussain

Salleh

Prasetyo

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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Real-life optimization problems demand robust algorithms that perform efficient search in the environment without trapping in local optimal locations. Such algorithms are equipped with balanced exploration and exploitation capabilities. Cuckoo search (CS) algorithm is also one of these optimization algorithms, which is inspired by nature. Despite effective search strategies such as Lévy flights and solution switching approach, CS suffers from a lack of population diversity when implemented in hard optimization problems. In this paper, enhanced local and global search strategies have been proposed in the CS algorithm. The proposed CS variant uses personal best information in solution generation process, hence called Personal Best Cuckoo Search (pBestCS). Moreover, instead of constant value for switching parameter, pBestCS dynamically updates switching parameter. The prior approach enhances local search ability, whereas the later modification enforces effective global search in the algorithm. The experimental results on test suite with different dimensions validated the efficiency of the proposed modification on optimization problems. Based on statistical and convergence analysis, pBestCS outperformed standard CS algorithm, as well as, particle swarm optimization (PSO) and artificial bee colony (ABC).

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A hybridization of cuckoo search and particle swarm optimization for solving optimization problems

Cited by 49 publications

References 43 publications

A Hybridization of Cuckoo Search and Differential Evolution for the Logistics Distribution Center Location Problem

A Hybridization of Cuckoo Search and Differential Evolution for the Logistics Distribution Center Location Problem

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

Personal Best Cuckoo Search Algorithm for Global Optimization

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