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

Zhiming Li, Yongquan Zhou, Sen Zhang and Junmin Song

Mathematical Problems in Engineering, 2016, vol. 2016, 1-22

Abstract:

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 Lévy-flight strategy, which is named as LMFO, is proposed. Lévy-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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1423930

DOI: 10.1155/2016/1423930

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