In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.(undefined
Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimi...
This paper applies a genetic algorithm with hierarchically structured population to solve unconstrai...
Particle swarm optimization (PSO) and Ant Colony Optimization (ACO) are two important methods of sto...
In this paper a new class of hybridization strategies between GA and PSO is presented and validated....
In this paper we test a particular variant of the (Repulsive) Particle Swarm method on some rather d...
This paper discusses the trade-off between accuracy, reliability and computing time in global optimi...
AbstractIn this paper, a new hybrid particle swarm optimization and genetic algorithm is proposed to...
University of Technology, Sydney. Faculty of Engineering and Information Technology.In decades, glob...
© 2020, The Author(s). Optimization problems can be found in many aspects of our lives. An optimizat...
AbstractThe purpose of this paper is to describe and evaluate a new algorithm for optimization. The ...
Particle Swarm Optimization (PSO), an evolutionary algorithm for optimization is extended to determ...
Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used ...
Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have...
This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combine...
Handling multi-objective optimization problems using evolutionary computations represents a promisin...
Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimi...
This paper applies a genetic algorithm with hierarchically structured population to solve unconstrai...
Particle swarm optimization (PSO) and Ant Colony Optimization (ACO) are two important methods of sto...
In this paper a new class of hybridization strategies between GA and PSO is presented and validated....
In this paper we test a particular variant of the (Repulsive) Particle Swarm method on some rather d...
This paper discusses the trade-off between accuracy, reliability and computing time in global optimi...
AbstractIn this paper, a new hybrid particle swarm optimization and genetic algorithm is proposed to...
University of Technology, Sydney. Faculty of Engineering and Information Technology.In decades, glob...
© 2020, The Author(s). Optimization problems can be found in many aspects of our lives. An optimizat...
AbstractThe purpose of this paper is to describe and evaluate a new algorithm for optimization. The ...
Particle Swarm Optimization (PSO), an evolutionary algorithm for optimization is extended to determ...
Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used ...
Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have...
This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combine...
Handling multi-objective optimization problems using evolutionary computations represents a promisin...
Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimi...
This paper applies a genetic algorithm with hierarchically structured population to solve unconstrai...
Particle swarm optimization (PSO) and Ant Colony Optimization (ACO) are two important methods of sto...