Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various tasks. However, MFO cannot provide competitive results for complex problems. The sinks into the local optimum due to rapid dropping of population diversity and poor exploration. Hence, this article, migration-based moth–flame (M-MFO) proposed address mentioned is...

This paper describes a newly developed Moth-Flame optimization algorithm to deal with optimal reactive power dispatch problem. The prime intention of reactive power dispatch problem is to curtail the real power loss and control the bus voltages in power system network. The Moth-Flame algorithm is one of the most powerful and robust new global optimization algorithms in engineering. The primary ...

Automatic programming (AP) is an important area of Machine Learning (ML) where computer programs are generated automatically. Swarm Programming (SP), a newly emerging research in AP, automatically generates the using Intelligence (SI) algorithms. This paper presents two grammar-based SP methods named as Grammatical Moth-Flame Optimizer (GMFO) and Whale (GWO). The Optimization algorithm used sea...

Abstract: Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption fo...

Abstract In the original Moth-Flame Optimization (MFO), search behavior of moth depends on corresponding flame and interaction between its flame, so it will get stuck in local optimum easily when facing multi-dimensional high-dimensional optimization problems. Therefore, this work, a generalized oppositional MFO with crossover strategy, named GCMFO, is presented to overcome mentioned defects. p...

This paper presents the application of Moth Flame optimization (MFO) algorithm to determine best impulse response coefficients FIR low pass, high band pass and stop filters. MFO was inspired by observing navigation strategy moths in nature called transverse orientation composed three mathematical sub-models. The performance proposed technique compared those other well-known performing technique...