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.
Highlights
Optimization is a process of finding the best possible solution(s) for a given problem
Aiming at the phenomenon that moth-flame optimization (MFO) algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Levy-flight strategy, which is named as Levy-flight mothflame optimization (LMFO), is proposed
An improved version of MFO algorithm based on Levy-flight strategy, which is named as LMFO, is proposed
Summary
Optimization is a process of finding the best possible solution(s) for a given problem. Some popular algorithms in this field are Genetic Algorithms (GA) [1, 2], Particle Swarm Optimization (PSO) [3], Ant Colony Optimization (ACO) [4], Evolutionary Strategy (ES) [5], Differential Evolution (DE) [6], and Evolutionary Programming (EP) [7]. Moth-flame optimization (MFO) [18] algorithm is a new metaheuristic optimization method through imitating the navigation method of moths in nature called transverse orientation. In this algorithm, moths and flames are both solutions. To improve the performance of MFO, a Levy-flight mothflame optimization (LMFO) algorithm is proposed.
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