GEM: A novel evolutionary optimization method with improved neighborhood search

DOI10.1016/j.amc.2009.01.009zbMath1162.90525OpenAlexW2056311241WikidataQ115463176 ScholiaQ115463176MaRDI QIDQ1021529

Ali Ahrari, Masoud Shariat-Panahi, Ali Asghar Atai

Publication date: 8 June 2009

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2009.01.009

zbMATH Keywords

evolutionary algorithmsstochastic optimization methodsmultimodal functionsAgent's territoryguided random searchmultiple global minimaoptimal search direction

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Stochastic programming (90C15)

Related Items (7)

On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms ⋮ Artificial bee colony algorithm combined with grenade explosion method and Cauchy operator for global optimization ⋮ On the utility of randomly generated functions for performance evaluation of evolutionary algorithms ⋮ Nonautonomous stochastic search in global optimization ⋮ On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation ⋮ Dimensional reduction in constrained global optimization on smooth manifolds ⋮ Nonautonomous stochastic search for global minimum in continuous optimization

Uses Software

• ABC
• nwSpGr

Cites Work

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• Likelihood approximation by numerical integration on sparse grids
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• Modifications of real code genetic algorithm for global optimization
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• An improved simulated annealing algorithm for bandwidth minimization
• Stability of evolutionary algorithms
• A genetic algorithm for the resource constrained multi-project scheduling problem
• Iterated local search for the quadratic assignment problem

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