The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Nelder-Mead Evolutionary Hybrid Algorithms
Abstract
Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular as they are derivativefree techniques, are not as prone to getting trapped in local minima, and can be tailored specifically to suit any given problem. The performance of evolutionary algorithms can be improved further by adding a local search component to them. The Nelder-Mead simplex algorithm (Nelder & Mead, 1965) is a simple local search algorithm that has been routinely applied to improve the search process in evolutionary algorithms, and such a strategy has met with great success. In this article, we provide an overview of the various strategies that have been adopted to hybridize two wellknown evolutionary algorithms - genetic algorithms (GA) and particle swarm optimization (PSO).
Related Content
|
Kamel Mouloudj, Vu Lan Oanh LE, Achouak Bouarar, Ahmed Chemseddine Bouarar, Dachel Martínez Asanza, Mayuri Srivastava.
© 2024.
20 pages.
|
|
José Eduardo Aleixo, José Luís Reis, Sandrina Francisca Teixeira, Ana Pinto de Lima.
© 2024.
52 pages.
|
|
Jorge Figueiredo, Isabel Oliveira, Sérgio Silva, Margarida Pocinho, António Cardoso, Manuel Pereira.
© 2024.
24 pages.
|
|
Fatih Pinarbasi.
© 2024.
20 pages.
|
|
Stavros Kaperonis.
© 2024.
25 pages.
|
|
Thomas Rui Mendes, Ana Cristina Antunes.
© 2024.
24 pages.
|
|
Nuno Geada.
© 2024.
12 pages.
|
|
|