Hybrid Taguchi-chaos of artificial bee colony algorithm for global numerical optimization

Jia Ping Tien, Tzuu-Hseng S. Li

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, a new evolutionary learning algorithm is proposed by hybridizing the Taguchi method and chaos artificial bee colony (CABC). The algorithm is thus called HTCABC. First, the chaos search algorithm and adaptive bound method is adopted to improve the ABC performance and convergence rate. Then, the Taguchi method and crossover operation are incorporated into the CABC to produce good food sources, thus accelerating the search capacity. The Taguchi method has also been utilized to establish a proper balance between the exploration and exploitation by incorporating the information from the best global solution into the solution search equation. Third, the natural phenomenon of the elite strategy is adopted and the recruitment of new scout bees is used for HTCABC, which can have a rapid convergence rate maintain the diversity of the population, and escape from local optima. Additionally, there is no complex parameter setting in the algorithm design. Therefore, the HTCABC can be a more robust, quickly convergent and more accurate optimal solution. Finally, the algorithm is examined by using a set of benchmarks and the proposed approach is effectively applied to solve the parameter identification of a chaotic system. Simulation results show that the proposed algorithm is more efficient than the existing algorithm reported in the literature.

Original languageEnglish
Pages (from-to)2665-2688
Number of pages24
JournalInternational Journal of Innovative Computing, Information and Control
Volume9
Issue number6
Publication statusPublished - 2013

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Numerical Optimization
Taguchi Method
Chaos theory
Global Optimization
Chaos
Taguchi methods
Convergence Rate
Evolutionary Learning
Algorithm Design
Parameter Identification
Global Solution
Chaotic System
Exploitation
Search Algorithm
Crossover
Evolutionary Algorithms
Learning Algorithm
Optimal Solution
Chaotic systems
Benchmark

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Information Systems
  • Software
  • Theoretical Computer Science

Cite this

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