A survey on optimization metaheuristics

By Ilhem Boussaïd, Julien Lepagnot, Patrick Siarry. Information Sciences, 2013

1,574 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/5463c6764909c0e6c637ce51385412c26c0e3e4c

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Citations in the corpus

SCA: A Sine Cosine Algorithm for solving optimization problems (2016, background): “Due to the drawbacks of the conventional optimization techniques, local optima stagnation, and the need to derivate the search space [1], a growing interest has been observed in stochastic optimization techniques [2] over the last two decades [3-5].”
Henry gas solubility optimization: A novel physics-based algorithm (2019, neutral): “Recently, metaheuristics have gained considerable popularity [3, 4, 5].”
Grasshopper optimization algorithm for multi-objective optimization problems (2017, background): “Although heuristics are very effective in solving real challenging problems, there are many difficulties when solving optimization problems [11].”
Grasshopper Optimisation Algorithm: Theory and application (2017, background): “Due to their simplicity, gradient-free mechanism, high local optima avoidance, and considering problems as black boxes, nature-inspired algorithms have been applied widely in science and industry [34-36].”
A survey on new generation metaheuristic algorithms (2019, background): “Burke et al. (2010, 2009, 2013) present an overview of hyperheuristic algorithms. Cowling et al. (2000) analyze different hyperheuristics for a real-world personnel scheduling problem. Burke et al. (2003) examine hyperheuristics and report evaluations on the timetabling problem. Chakhlevitch and Cow”
An Optimization Algorithm for Solving Systems of Singular Boundary Value Problems (2014, background): “On the other aspect as well, optimization problems can be divided into several categories depending on whether they are continuous or discrete, constrained or unconstrained, single-objective or multi-objective, static or dynamic [16].”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “Furthermore, it is worthy to note that metaheuristic algorithms can be conveniently implemented for global optimization (BoussaïD et al., 2013).”
Combinatorial optimization and reasoning with graph neural networks (2021, background): “Historically, the optimization and theoretical computer science communities have been focusing on finding optimal (Korte and Vygen, 2012), heuristic (Boussäıd et al., 2013), or approximate (Vazirani, 2010) solutions for individual problem instances.”