Multi-Objective Optimization Using Evolution Strategies (original) (raw)
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MultiObjective Optimization using Evolutionary Computation Techniques
International Journal of Computer Applications, 2011
The present paper gives an overview of different versions of Evolution Strategies, namely the (1+1) Evolution Strategy, the Higher Order (µ/ρ, λ ) Evolution Strategy and the Niching [κ(µ/ρ, λ )] Evolution Strategy, and how these methods can be applied to problems in Electrical Engineering. Significant features of the algorithms implemented by the authors are presented. Finally, results are discussed on three electromagnetic optimization problems.
Electromagnetic device optimization by hybrid evolution strategy approaches
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2007
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. Practical implications -The proposed approach appears to be an efficient general purpose optimizer for electromagnetic design problems. Originality/value -This paper introduces the use of chaotic sequences in the area of electromagnetic design optimization.
Higher order evolution strategies for the global optimization of electromagnetic devices
IEEE Transactions on Magnetics, 1993
Abstmct -Basic evolution strategies @S) utilizing simplified features of biological evolution like mutation and selection are applied to solve problems of parameter identification for the optimal design of electromagnetic devices. Due to the fact that objective functions of real world applications usually have more than one minimum, additional features can be added to minimize the risk of getting trapped in a local minimum. Finite life span, recombination and population, applied in @/&) evolution strategies or a "disaster" with an unnatural high stepwidth after a certain number of generations can help to arrive at a reliable solution within a reasonable computational effort.
IEEE Transactions on Magnetics, 2000
The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective differential evolutionary algorithm, only use conventional DE strategies, and, in order to optimize performance constrains problems, the feasibility of the solutions was considered only at selection step. This paper presents a new multiobjective evolutionary algorithm based on differential evolution. In the mutation step, the proposed method which applied multiguiders instead of conventional base vector selection method is used. Therefore, feasibility of multiguiders, involving constraint optimization problems, is also considered. Furthermore, the approach also incorporates nondominated sorting method and secondary population for the nondominated solutions. The propose algorithm is compared with resent approaches of multiobjective optimizers in solving multiobjective version of Testing Electromagnetic Analysis Methods (TEAM) problem 22.
A hybrid multiobjective differential evolution method for electromagnetic device optimization
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2011
Purpose -The purpose of this paper is to show that the performance of differential evolution (DE) can be substantially improved by a combination of techniques. These enhancements are applicable to both single and multiobjective problems. Their combined use allows the optimization of complex 3D electromagnetic devices. Design/methodology/approach -DE is improved by a combination of techniques which are applied in a cascade way and their single and combined effect is tested on well-known benchmarks and domain-specific applications. Findings -It is shown that the combined use of enhancement techniques provides substantial improvements in the speed of convergence for both single and multiobjective problems.
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2009
Purpose -The purpose of this paper is to show, on a widely used benchmark problem, that adaptive mutation factors and attractive/repulsive phases guided by population diversity can improve the search ability of differential evolution (DE) algorithms. Design/methodology/approach -An adaptive mutation factor and attractive/repulsive phases guided by population diversity are used within the framework of DE algorithms. Findings -The paper shows that the combined use of adaptive mutation factors and population diversity in order to guide the attractive/repulsive behavior of DE algorithms can provide high-quality solutions with small standard deviation on the selected benchmark problem. Research limitations/implications -Although the chosen benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results. Practical implications -The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems. Originality/value -This paper introduces the use of population diversity in order to guide the attractive/repulsive behavior of DE algorithms.
A new hybrid evolutionary algorithm for high dimension electromagnetic problems
2005
In this paper the authors present a new hybrid evolutionary algorithm, particularly suitable for high dimension electromagnetic problems. This method, called GSO, Genetical Swarm Optimization, essentially combines the features of other two well known evolutionary algorithms, the Genetic Algorithms and Particle Swarm Optimization.
IEEE Transactions on Magnetics, 2011
An effective methodology for a robust global optimization of electromagnetic devices is developed based on the gradient index and multi-objective optimization method. The method transforms a given optimization problem into a multi-objective optimization one by adding another optimization target for minimizing the gradient index. The performance and robustness of the obtained optimal designs from the proposed algorithm are investigated through a numerical experiment with the TEAM Workshop Problem 22.
Multiobjective Optimization in Computational Electromagnetics
In this paper we show how multiobjective optimization can be applied to elec- tromagnetic problems. The optimization algorithms are combined with CAD and mesh generation software, and electromagnetic solvers. Three dieren t multiobjective optimization methods are applied: one evolutionary method, one method based on scalarizing of the objectives combined with a method for single objective optimization and a multiobjective respond surface method. To demonstrate the procedure we study the optimization of the return loss of a patch antennas at two dieren t frequencies.