Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems

By Zhiming Li, Yongquan Zhou, Sen Zhang, Junmin Song. 2016

117 citations recorded by the source corpus.

The moth-flame optimization (MFO) algorithm is a novel nature-inspired heuristic paradigm. The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a spiral path around artificial lights. Aiming at the phenomenon that MFO algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Levy-flight strategy, which is named as LMFO, is proposed. Levy-flight can increase the diversity of the population against premature convergence and make the algorithm jump out of local optimum more effectively. This approach is helpful to obtain a better trade-off between exploration and exploitation ability of MFO, thus, which can make LMFO faster and more robust than MFO. And a comparison with ABC, BA, GGSA, DA, PSOGSA, and MFO on 19 unconstrained benchmark functions and 2 constrained engineering design problems is tested. These results demonstrate the superior performance of LMFO.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/61e168c5345d7e0e0587d8f64015b246b6800475

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In Artificial Intelligence

1. Huiling Chen
2. Bo-Seok Yang
3. Hui Huang
4. Xuehua Zhao
5. Lufeng Hu
6. Zhennao Cai
7. Changfei Tong
8. Mingjing Wang

In Computational Theory and Mathematics

1. Bo-Seok Yang
2. Hui Huang
3. Xuehua Zhao
4. Lufeng Hu
5. Zhennao Cai
6. Changfei Tong
7. Mingjing Wang
8. Huiling Chen

In Molecular Biology

1. Huiling Chen

Citations in the corpus

• Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses (2017, background): “[30] has also put forward an improved version of MFO based on Lévy-flight (LMFO) and has successfully solved the problem of engineering optimization.”