An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges
As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
[HTML][HTML] Differential evolution: A recent review based on state-of-the-art works
Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin's theory of
evolution and has been studied extensively to solve different areas of optimisation and …
evolution and has been studied extensively to solve different areas of optimisation and …
Metaheuristics: a comprehensive overview and classification along with bibliometric analysis
Research in metaheuristics for global optimization problems are currently experiencing an
overload of wide range of available metaheuristic-based solution approaches. Since the …
overload of wide range of available metaheuristic-based solution approaches. Since the …
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm
S Mirjalili - Knowledge-based systems, 2015 - Elsevier
In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame
Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method …
Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method …
Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations
In recent algorithmic family simulates different biological processes observed in Nature in
order to efficiently address complex optimization problems. In the last years the number of …
order to efficiently address complex optimization problems. In the last years the number of …
A survey on optimization metaheuristics
I Boussaïd, J Lepagnot, P Siarry - Information sciences, 2013 - Elsevier
Metaheuristics are widely recognized as efficient approaches for many hard optimization
problems. This paper provides a survey of some of the main metaheuristics. It outlines the …
problems. This paper provides a survey of some of the main metaheuristics. It outlines the …
[HTML][HTML] Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms
Meta heuristics is an optimization approach that works as an intelligent technique to solve
optimization problems. Evolutionary algorithms, human-based algorithms, physics-based …
optimization problems. Evolutionary algorithms, human-based algorithms, physics-based …
Supply-demand-based optimization: A novel economics-inspired algorithm for global optimization
W Zhao, L Wang, Z Zhang - Ieee Access, 2019 - ieeexplore.ieee.org
A novel metaheuristic optimization algorithm, named supply-demand-based optimization
(SDO), is presented in this paper. SDO is a swarm-based optimizer motivated by the supply …
(SDO), is presented in this paper. SDO is a swarm-based optimizer motivated by the supply …
Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients
In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-
objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm …
objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm …
Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption
W Qiao, H Moayedi, LK Foong - Energy and Buildings, 2020 - Elsevier
Current study aimed to combine the multi-layer Perceptron (MLP) neural network technique
with five metaheuristic computational algorithms, namely invasive weed optimization (IWO …
with five metaheuristic computational algorithms, namely invasive weed optimization (IWO …