Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is
widely used to solve different optimization problems. However, MFO and its variants …

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths
toward the light source is an effective approach to solve global optimization problems …

A growing trend of introducing new metaheuristic algorithms and their improvements is
observed with almost the same inherited weaknesses. The main reason is that a few studies …

Moth-flame optimization (MFO) is a widely used nature-inspired algorithm characterized by a
simple structure with simple parameters. However, for some complex optimization tasks …

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that
demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO …

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based
on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is …

Moth-flame optimization algorithm (MFO) is a new nature-inspired meta-heuristic based on
the navigation routine of moths in the environment known as transverse orientation. For …

Evolutionary population-based methods have found their applications in dealing with many
real-world simulation experiments and mathematical modelling problems. The Moth-flame …

Abstract Moth–Flame Optimization (MFO) is widely utilized to solve optimization problems in
different fields since it has a simple structure and easy implementation. However, MFO …

Moth flame optimization (MFO) is a swarm-based algorithm with mediocre performance and
marginal originality proposed in recent years. It tried to simulate the fantasy navigation mode …