Metaheuristics: From Design to Implementation

By El-Ghazali Talbi. 2009

3,342 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/52ff98489e49632eb3626696f5f58b948b6dbbe4

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In Control and Systems Engineering

In Management Information Systems

Citations in the corpus

Harris hawks optimization: Algorithm and applications (2019, methodology): “[6] 13 In general, metaheuristic algorithms have two types [7]; single solution based (i.”
Bat algorithm for multi-objective optimisation (2011, background): “Metaheuristic algorithms are very powerful in dealing with this kind of optimization, and there are many review articles and excellent textbooks (Coello, 1999; Deb, 2001; Isasi and Hernandez, 2004; Yang, 2008; Talbi, 2009; Yang, 2010c).”
Big Bang-Big Crunch optimization for parameter estimation in structural systems (2010, background): “optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) have been successfully applied to a variety of optimization problems such as wire routing, scheduling, traveling salesman, image processing, engineering design, parameter fitting, computer game playing, knapsack pr”
Chaos-enhanced accelerated particle swarm optimization (2013, background): “These techniques attempt to mimic some characteristics of natural phenomena or social behavior [2].”
A review of metaheuristic scheduling techniques in cloud computing (2015, background): “Metaheuristic based techniques [3] deal with these problems by providing near optimal solutions within reasonable time.”
Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art (2021, methodology): “Furthermore, MHs can make deterministic or stochastic decisions during the search process to solve optimization problems ( Talbi, 2009 ).”
Krill herd: A new bio-inspired optimization algorithm (2012, background): “The specific objectives of developing modern metaheuristic algorithms are to solve problems faster, to solve large problems, and to obtain more robust methods [3].”
Resource Allocation in NFV: A Comprehensive Survey (2016, background): “As the optimal solution for large instances of these problems is hard to find, metaheuristics can be used to find near-optimal solutions by iteratively improve problem solutions with regard to a given measure of quality [78].”