A novel genetic algorithm based method for solving continuous nonlinear optimization problems through subdividing and labeling

[Display omitted] •We introduce a novel algorithm (SLGA) to solve continuous nonlinear optimization problems.•SLGA defines an integer label on a polytope built on a n-dimensional search space.•SLGA applies a crossover operator to approach the optimum by reducing the search space.•New population is g...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation Vol. 115; pp. 27 - 38
Main Authors: Esmaelian, Majid, Tavana, Madjid, Santos-Arteaga, Francisco J., Vali, Masoumeh
Format: Journal Article
Language:English
Published: London Elsevier Ltd 01.02.2018
Elsevier Science Ltd
Subjects:
Combinatorial analysis
Continuity (mathematics)
Evolutionary algorithms
Fitness function
Genetic algorithm (GA)
Genetic algorithms
Iterative methods
Labeling
Mathematical analysis
Nonlinear equations
Nonlinear optimization
Operators (mathematics)
Optimization
Optimization algorithms
Studies
Subdividing labeling
Genetic algorithm (GA)
Subdividing labeling
Fitness function
Nonlinear optimization
ISSN:0263-2241, 1873-412X
Online Access:Get full text
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Summary:[Display omitted] •We introduce a novel algorithm (SLGA) to solve continuous nonlinear optimization problems.•SLGA defines an integer label on a polytope built on a n-dimensional search space.•SLGA applies a crossover operator to approach the optimum by reducing the search space.•New population is generated by subdividing the space and applying mutation and crossover.•Experiments show enhanced convergence capabilities of SLGA relative to other methods. We introduce a novel method called subdividing labeling genetic algorithm (SLGA) to solve optimization problems involving n – dimensional continuous nonlinear functions. SLGA is based on the mutation and crossover operators of genetic algorithms, which are applied on a subdivided search space where an integer label is defined on a polytope built on the n – dimensional space. The SLGA method approaches a global optimal solution by reducing the feasible search region in each iteration. One of its main advantages is that it does not require computing the derivatives of the objective function to guarantee convergence. We apply the SLGA method to solve optimization problems involving complex combinatorial and large-scale systems and illustrate numerically how it outperforms several other competing algorithms such as Differential Evolution even when considering problems with a large number of elements.
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ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2017.09.034