Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

Wang, Gai-Ge; Gandomi, Amir H.; Zhao, Xiangjun; Chu, Hai Cheng

doi:10.1007/s00500-014-1502-7

Cited by 216 publications

(69 citation statements)

References 46 publications

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“…The well-known metaheuristics include the Krill Herd (KH) algorithm [4][5][6], the Cuckoo algorithm [7,8], and the Monarch Butterfly Optimization (MBO) [9,10], which can improve efficiency by using the Lévy flight operator and fly straight operator [11], the Hybridizing Harmony Search [12], the Bat algorithm [13], the Elephant Herding Optimization (EHO) [14], or the Earthworm optimization algorithm (EWA) [15].…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Ketsripongsa

Pitakaso

Sethanan

et al. 2018

MCA

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Abstract:This research aimed to solve the economic crop planning problem, considering transportation logistics to maximize the profit from cultivated activities. Income is derived from the selling price and production rate of the plants; costs are due to operating and transportation expenses. Two solving methods are presented: (1) developing a mathematical model and solving it using Lingo v.11, and (2) using three improved Differential Evolution (DE) Algorithms-I-DE-SW, I-DE-CY, and I-DE-KV-which are DE with swap, cyclic moves (CY), and K-variables moves (KV) respectively. The algorithms were tested by 16 test instances, including this case study. The computational results showed that Lingo v.11 and all DE algorithms can find the optimal solution eight out of 16 times. Regarding the remaining test instances, Lingo v.11 was unable to find the optimal solution within 400 h. The results for the DE algorithms were compared with the best solution generated within that time. The DE solutions were 1.196-1.488% better than the best solution generated by Lingo v.11 and used 200 times less computational time. Comparing the three DE algorithms, MDE-KV was the DE that was the most flexible, with the biggest neighborhood structure, and outperformed the other DE algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Ketsripongsa

Pitakaso

Sethanan

et al. 2018

MCA

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“…Harmony Search optimization algorithm is combined with Cuckoo search algorithm to enhance the search ability of the cuckoo search (CS) algorithm [16]. In this method, the pitch adjustment operation in harmony search is considered as a mutation operator to speed up the convergence process of the updating the cuckoo search.…”

Section: Related Workmentioning

confidence: 99%

CLUSTERING CATEGORICAL DATA USING k-MODES BASED ON CUCKOO SEARCH OPTIMIZATION ALGORITHM

Lakshmi¹,

Visalakshi²,

Shanthi³

et al. 2017

IJSC

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Cluster analysis is the unsupervised learning technique that finds the interesting patterns in the data objects without knowing class labels. Most of the real world dataset consists of categorical data. For example, social media analysis may have the categorical data like the gender as male or female. The k-modes clustering algorithm is the most widely used to group the categorical data, because it is easy to implement and efficient to handle the large amount of data. However, due to its random selection of initial centroids, it provides the local optimum solution. There are number of optimization algorithms are developed to obtain global optimum solution. Cuckoo Search algorithm is the population based metaheuristic optimization algorithms to provide the global optimum solution. Methods: In this paper, k-modes clustering algorithm is combined with Cuckoo Search algorithm to obtain the global optimum solution. Results: Experiments are conducted with benchmark datasets and the results are compared with k-modes and Particle Swarm Optimization with k-modes to prove the efficiency of the proposed algorithm.

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“…There are some studies on hybrid harmony search and other methods, like the combination of harmony search and firefly algorithm 64 , cuckoo search algorithm 65 , but most of the hybridization is adopted the standard harmony search which has some shortcomings. The parameters are difficult to adjust, for example.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

Feng¹,

Wang²,

Gao³

2016

IJCIS

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Cuckoo search (CS) is a novel biologically inspired algorithm and has been widely applied to many fields. Although some binary-coded CS variants are developed to solve 0-1 knapsack problems, the search accuracy and the convergence speed are still needed to further improve. According to the analysis of the shortcomings of the standard CS and the advantage of the global harmony search (GHS), a novel hybrid meta-heuristic optimization approach, called cuckoo search Algorithm with global harmony search (CSGHS), is proposed in this paper to solve 0-1 knapsack problems (KP) more effectively. In CSGHS, it is the combination of the exploration of GHS and the exploitation of CS that makes the CSGHS efficient and effective. The experiments conducted demonstrate that the CSGHS generally outperformed the binary cuckoo search, the binary shuffled frog-leaping algorithm and the binary differential evolution in accordance with the search accuracy and convergence speed. Therefore, the proposed hybrid algorithm is effective to solve 0-1 knapsack problems.

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Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

Cited by 216 publications

References 46 publications

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

CLUSTERING CATEGORICAL DATA USING k-MODES BASED ON CUCKOO SEARCH OPTIMIZATION ALGORITHM

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

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