Purpose - This paper aims to show on a widely used benchmark problem that chaotic sequences can improve the search ability of evolution strategies (ES). Design/methodology/approach - The Lozi map is used to generate new individuals in the framework of ES algorithms. A quasi-Newton (QN) method is also used within the iterative loop to improve the solution's quality locally. Findings - It is shown that the combined use of chaotic sequences and QN methods can provide high-quality solutions with small standard deviation on the selected benchmark problem. Research limitations/implications - Although the benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results. Pr...
In this paper, a greedy Genetic Algorithm for continuous variables electromagnetic optimization prob...
A new effective optimization algorithm suitably developed for electromagnetic applications called ge...
This paper presents a new hybrid evolutionary algorithm combining Particle Swarm Optimization and G...
Over the past few years, the field of global optimization has been very active, producing different ...
As we all know, traditional electromagnetism mechanism (EM) algorithm has the disadvantage with low ...
Purpose – The purpose of this paper is to show, on a widely used benchmark problem, that adaptive mu...
Recently, evolutionary algorithms (e.g. genetic algorithms, evolutionary programming, and evolution ...
Purpose – The purpose of this paper is to show that the performance of differential evolution (DE) c...
In this paper, an investigation of the behavior of a recently defined hybrid algorithm for continuou...
Real-world engineering optimization problems involve multiple design factors and constraints and con...
This paper presents and compares three topology optimization tools differing from the optimization a...
Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions...
Device optimization using metaheuristic methods has been successfully applied to electromagnetic dev...
This paper gives an overview of some stochastic optimization strategies, namely, evolution strategie...
The role of the parameters uncertainness in the optimal design of electromagnetic devices is discuss...
In this paper, a greedy Genetic Algorithm for continuous variables electromagnetic optimization prob...
A new effective optimization algorithm suitably developed for electromagnetic applications called ge...
This paper presents a new hybrid evolutionary algorithm combining Particle Swarm Optimization and G...
Over the past few years, the field of global optimization has been very active, producing different ...
As we all know, traditional electromagnetism mechanism (EM) algorithm has the disadvantage with low ...
Purpose – The purpose of this paper is to show, on a widely used benchmark problem, that adaptive mu...
Recently, evolutionary algorithms (e.g. genetic algorithms, evolutionary programming, and evolution ...
Purpose – The purpose of this paper is to show that the performance of differential evolution (DE) c...
In this paper, an investigation of the behavior of a recently defined hybrid algorithm for continuou...
Real-world engineering optimization problems involve multiple design factors and constraints and con...
This paper presents and compares three topology optimization tools differing from the optimization a...
Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions...
Device optimization using metaheuristic methods has been successfully applied to electromagnetic dev...
This paper gives an overview of some stochastic optimization strategies, namely, evolution strategie...
The role of the parameters uncertainness in the optimal design of electromagnetic devices is discuss...
In this paper, a greedy Genetic Algorithm for continuous variables electromagnetic optimization prob...
A new effective optimization algorithm suitably developed for electromagnetic applications called ge...
This paper presents a new hybrid evolutionary algorithm combining Particle Swarm Optimization and G...