A critical review of moth-flame optimization algorithm and its variants: structural reviewing, performance evaluation, and statistical analysis

H Zamani, MH Nadimi-Shahraki, S Mirjalili… - … Methods in Engineering, 2024 - Springer
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 …

[HTML][HTML] A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution

MS Shaikh, S Raj, R Babu, S Kumar… - Decision Analytics …, 2023 - Elsevier
The transmission lines are used for power distribution across large distances. Different
parameters affect the power transmission efficiency, and the quality of service. Furthermore …

An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy

SK Sahoo, AK Saha, S Nama, M Masdari - Artificial Intelligence Review, 2023 - Springer
Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization
algorithm based on the moth's movement towards the moon. Premature convergence and …

An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis

H Zamani, MH Nadimi-Shahraki - Biomedical Signal Processing and …, 2024 - Elsevier
Artificial neural network (ANN) is an information processing paradigm that loosely models
the thinking patterns of the human brain with specifications such as real-time learning, self …

Non-dominated sorting advanced butterfly optimization algorithm for multi-objective problems

S Sharma, N Khodadadi, AK Saha… - Journal of Bionic …, 2023 - Springer
This paper uses the Butterfly Optimization Algorithm (BOA) with dominated sorting and
crowding distance mechanisms to solve multi-objective optimization problems. There is also …

A hybrid moth flame optimization algorithm for global optimization

SK Sahoo, AK Saha - Journal of Bionic Engineering, 2022 - Springer
Abstract The Moth Flame Optimization (MFO) algorithm shows decent performance results
compared to other meta-heuristic algorithms for tackling non-linear constrained global …

Multi-population-based adaptive sine cosine algorithm with modified mutualism strategy for global optimization

AK Saha - Knowledge-Based Systems, 2022 - Elsevier
The sine cosine algorithm (SCA) is a population-based metaheuristic strategy that has been
demonstrated competitive performance and has received significant attention from scientists …

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

SK Sahoo, EH Houssein, M Premkumar… - Expert Systems with …, 2023 - Elsevier
The COVID-19 is one of the most significant obstacles that humanity is now facing. The use
of computed tomography (CT) images is one method that can be utilized to recognize …

Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection

NK Hussein, M Qaraad, S Amjad… - Journal of …, 2023 - academic.oup.com
The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-
heuristic used to solve optimization problems. The MFO algorithm, which employs moths' …

[HTML][HTML] Centroid opposition-based backtracking search algorithm for global optimization and engineering problems

S Debnath, S Debbarma, S Nama, AK Saha… - … in Engineering Software, 2024 - Elsevier
Evolutionary algorithms (EAs) have a lot of potential to handle nonlinear and non-convex
objective functions. Particularly, the backtracking search algorithm (BSA) is a popular nature …