Journal
KNOWLEDGE-BASED SYSTEMS
Volume 62, Issue -, Pages 69-83Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2014.02.021
Keywords
Fruit fly optimization; Evolutionary algorithms; Meta-heuristics; Continuous optimization; Harmony search
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Funding
- National Science Foundation of China [61174187]
- Program for New Century Excellent Talents in University [NCET-13-0106]
- Specialized Research Fund for the Doctoral Program of Higher Education [20130042110035]
- Science Foundation of Liaoning Province in China [2013020016]
- Basic Scientific Research Foundation of Northeast University [N110208001]
- Starting Foundation of Northeast University [29321006]
- IAPI Fundamental Research Funds [2013ZCX04-04]
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This paper presents an improved fruit fly optimization (IFFO) algorithm for solving continuous function optimization problems. In the proposed IFFO, a new control parameter is introduced to tune the search scope around its swarm location adaptively. A new solution generating method is developed to enhance accuracy and convergence rate of the algorithm. Extensive computational experiments and comparisons are carried out based on a set of 29 benchmark functions from the literature. The computational results show that the proposed IFFO not only significantly improves the basic fruit fly optimization algorithm but also performs much better than five state-of-the-art harmony search algorithms. (C) 2014 Elsevier B.V. All rights reserved,
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