Add to ORKGThe paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel multi-objective Particle Swarm Optimization (PSO). The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM). The hit rate and the quality of the Pareto front distribution are classified
In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-...
Recently there is an increasing attention on some novel techniques among Evolutionary Optimization a...
In this paper a new effective optimization algorithm called Genetical Swarm Optimization (GSO) is pr...
Part 2: Optimization-Genetic AlgorithmsInternational audienceMany real-world problems can be formula...
Abstract: Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-ob...
Abstract- Although many methods for dealing with multi-objective optimisation (MOO) problems are ava...
In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (...
This paper presents a comprehensive review of a multi-objective particle swarm optimization (MOPSO) ...
In recent years there has been an increasing attention to some novel evolutionary techniques, such a...
Author name used in this publication: S. L. HoAuthor name used in this publication: G. Z. Ni2006-200...
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimizat...
In recent years there has been an increasing attention to some novel evolutionary techniques, such a...
In this paper a new effective optimization algorithm suitably developed for electromagnetic applicat...
In this paper a new effective optimization algorithm called genetical swarm optimization (GSO) is pr...
In this paper a new effective optimization algorithm suitably developed for electromagnetic applicat...
In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-...
Recently there is an increasing attention on some novel techniques among Evolutionary Optimization a...
In this paper a new effective optimization algorithm called Genetical Swarm Optimization (GSO) is pr...
Part 2: Optimization-Genetic AlgorithmsInternational audienceMany real-world problems can be formula...
Abstract: Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-ob...
Abstract- Although many methods for dealing with multi-objective optimisation (MOO) problems are ava...
In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (...
This paper presents a comprehensive review of a multi-objective particle swarm optimization (MOPSO) ...
In recent years there has been an increasing attention to some novel evolutionary techniques, such a...
Author name used in this publication: S. L. HoAuthor name used in this publication: G. Z. Ni2006-200...
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimizat...
In recent years there has been an increasing attention to some novel evolutionary techniques, such a...
In this paper a new effective optimization algorithm suitably developed for electromagnetic applicat...
In this paper a new effective optimization algorithm called genetical swarm optimization (GSO) is pr...
In this paper a new effective optimization algorithm suitably developed for electromagnetic applicat...
In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-...
Recently there is an increasing attention on some novel techniques among Evolutionary Optimization a...
In this paper a new effective optimization algorithm called Genetical Swarm Optimization (GSO) is pr...