Design optimization of power objects based on Constrained non-linear minimization, Genetic algorithms, Particle swarm optimization algorithms and Differential Evolution Algorithms
Journal
International Journal on Information Technologies and Security
Date Issued
2014-09-01
Author(s)
Salkoski, Rasim
Abstract
This paper gives a detailed comparative analysis of Constrained
non-linear minimization (CN), Genetic Algorithms (GA), Particle Swarm
Optimization Algorithms (PSO) and Differential Evolution Algorithms
(DE) results. The Objective Function that is optimized is a minimization
dependent and all constraints are normalized and modeled as inequalities.
The results demonstrate the potential of the DE Algorithm, shows its
effectiveness and robustness to solve the optimal power object.
non-linear minimization (CN), Genetic Algorithms (GA), Particle Swarm
Optimization Algorithms (PSO) and Differential Evolution Algorithms
(DE) results. The Objective Function that is optimized is a minimization
dependent and all constraints are normalized and modeled as inequalities.
The results demonstrate the potential of the DE Algorithm, shows its
effectiveness and robustness to solve the optimal power object.
Subjects
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