Chaotic Krill Herd algorithm

Wang, Gai-Ge; Guo, Lihong; Gandomi, Amir H.; Hao, Guo-Sheng; Wang, Heqi

doi:10.1016/j.ins.2014.02.123

Cited by 516 publications

(215 citation statements)

References 42 publications

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“…Elitism strategy can forbid the best ones being destroyed by three krill motions, and can guarantee the population can always proceed to the better status. Limited by the length of the paper, the more detailed process of elitism strategy can be referred to in [4,63].…”

Section: Kh Methods With Elitism Strategy (Khe)mentioning

confidence: 99%

A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

Wang

2015

Algorithms

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As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH) algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.

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Section: Kh Methods With Elitism Strategy (Khe)mentioning

confidence: 99%

A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

Wang

2015

Algorithms

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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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“…us in some cases, the algorithm development is based on chaotic variables instead of random variables, and these algorithms are called chaotic algorithms [22][23][24][25][26]. A chaotic Firefly Algorithm was proposed in [22].…”

Section: Introductionmentioning

confidence: 99%

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

Saxena

Shekhawat

Kumar

2018

Modelling and Simulation in Engineering

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In recent years, metaheuristic algorithms have revolutionized the world with their better problem solving capacity. Any metaheuristic algorithm has two phases: exploration and exploitation. e ability of the algorithm to solve a difficult optimization problem depends upon the efficacy of these two phases. ese two phases are tied with a bridging mechanism, which plays an important role. is paper presents an application of chaotic maps to improve the bridging mechanism of Grasshopper Optimisation Algorithm (GOA) by embedding 10 different maps. is experiment evolves 10 different chaotic variants of GOA, and they are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). e performance of these variants is tested over ten shifted and biased unimodal and multimodal benchmark functions. Further, the applications of these variants have been evaluated on three-bar truss design problem and frequency-modulated sound synthesis parameter estimation problem. Results reveal that the chaotic mechanism enhances the performance of GOA. Further, the results of the Wilcoxon rank sum test also establish the efficacy of the proposed variants.

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Chaotic Krill Herd algorithm

Cited by 516 publications

References 42 publications

A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

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

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

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