Objective function “stretching” to alleviate convergence to local minima
By Konstantinos E. Parsopoulos, Vassilis P. Plagianakos, G. Magoulas, Michael N. Vrahatis. 2001
129 citations recorded by the source corpus.
Subfields: Mechanical Engineering, Statistics and Probability, Theoretical Computer Science
View Paper: https://www.semanticscholar.org/paper/f45cffebe6653b974bf4febc99235d3ad53ec649
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In Mechanical Engineering
In Statistics and Probability
In Theoretical Computer Science
Citations in the corpus
- An analysis of particle swarm optimizers (2002, unknown): “[101, 99, 98, 97].”
- Particle swarm optimization method in multiobjective problems (2002, methodology): “PSO has proved to be efficient at solving Unconstrained Global Optimization and engineering problems [4, 10, 11, 12, 13, 17].”
- Recent approaches to global optimization problems through Particle Swarm Optimization (2002, unknown): “At this point, it is useful to illustrate the impact of the two transformations suggested by the Function “Stretching” technique, on a well known optimization test function (Parsopoulos et al., 2001a; Parsopoulos et al., 2001b, ”
- Particle swarm optimization for integer programming (2002, background): “The PSO technique has proved to be very e ective in solving real valued global optimization problems, in static, noisy as well as continuously changing environments, and for performing neural networks training [19]{ [22], exhibiting competitive results with the EA [1].”
- On the computation of all global minimizers through particle swarm optimization (2004, methodology): “A different recently proposed technique, developed to address the problem of local minima, is the stretching technique [26], [64], [67], [68].”
- Particle Swarm Optimization Method for Constrained Optimization Problems (2002, methodology): “The PSO technique has proven to be very e cient for solving real valued global unconstrained optimization problems [13]{[17].”
- Particle Swarm Methods (2018, methodology): “The Stretching technique consists of a two-stage transformation of the objective function [95],”