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Publication
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| Title of Article |
RO-FOA: An Ecosystem-Inspired Compact Fruit Fly Optimization Algorithm for Box-Constrained Optimization |
| Date of Acceptance |
9 August 2019 |
| Journal |
| Title of Journal |
Engineering and Applied science Research (EASR) |
| Standard |
SCOPUS |
| Institute of Journal |
Engineering and Applied Science Research (EASR) Faculty of Engineering, Khon Kaen University |
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| Year of Publication |
2019 |
| Page |
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| Abstract |
The fruit fly optimization algorithm (FOA) was a recently proposed. The FOA has a number of advantages over other nature-inspired algorithms such as its simple structure and ease of implementation. However, the FOA’s search procedures present a problem. FOA has a low success rate search and a slow convergence speed, when it has to deal with complex problems. This is because FOA generates a new position around its swarm location by random uniform distribution. To eliminate this drawback, our paper presents an improved fruit fly algorithm called RO-FOA. The RO-FOA technique takes knowledge of mutualistic relationship that is a form of relationship in ecosystems and biological theory. Our strategy blends two popular algorithms, i.e., the random walk (RW) and the opposition-based learning (OBL) algorithm, to establish the two-characteristic swarm for searching procedures. RO-FOA’s structure is very compact as the implementation uses only three fruit flies. Furthermore, the advantages of including two-characteristic population and dynamic distribution adaptation in the evolving process can produce an algorithm with the necessary search efficiency to find the optimal solution. A comprehensive set of 34 benchmark functions, containing a wide range of dimensions were used to validate the capability of the proposed algorithm. The results show that RO-FOA outperformed the existing FOA, as well as seven comparative well known meta-heuristic algorithms. RO-FOA can efficiently train multi-layer perceptrons for 5-bit and 8-bit auto-encoder problems. These results demonstrate that the RO-FOA can enhance the diversity of population distribution, solution quality and the convergence rate of the algorithm. |
| Keyword |
Optimization algorithm, Nature-inspired algorithm, Fruit fly optimization algorithm, Meta-heuristics, Ecosystem, Mutualistic relationship |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ได้รับการตอบรับให้ตีพิมพ์ |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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