Multiobjective Tabu Search Algorithms for Optimal Design of Electromagnetic Devices (original) (raw)

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.

Engineering design optimisation using tabu search

2000

This paper describes an optimisation methodology that has been specifically developed for engineering design problems. The methodology is based on a Tabu search (TS) algorithm that has been shown to find high quality solutions with a relatively low number of objective function evaluations. Whilst the methodology was originally intended for a small range of design problems it has since been successfully applied to problems from different domains with no alteration to the underlying method. This paper describes the method and it's application to three different problems. The first is from the field of structural design, the second relates to the design of electromagnetic pole shapes and the third involves the design of turbomachinery blades.

Optimal design of electromagnetic devices: Development of an efficient optimization tool based on smart mutation operations implemented in a genetic algorithm

Mathematics and Computers in Simulation, 2013

Topology optimization methods are aimed to produce optimal design. These tools implement optimization algorithms that modify the distribution of some materials within a predefined design space without a priori ideas regarding the topology or the geometry of the best solution. In this paper, we study a specific tool that combines a genetic algorithm, a material distribution formalism based on Voronoi cells and a commercial FEM evaluation tool. In particular, this paper shows, through a simple but representative case study, that it is possible to improve the performance of the topology optimization tool during the local search phase, i.e. the geometrical and dimensional optimization phase for which the topology optimization methods are originally not well-suited.

A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization

IEEE Transactions on Emerging Topics in Computational Intelligence, 2019

Optimization efficiency is a major challenge for electromagnetic (EM) device, circuit and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogate model-aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity.

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.

Electromagnetic optimization based on an improved diversity-guided differential evolution approach and adaptive mutation factor

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.

Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm

This paper presents a physics-inspired metaheuristic optimization algorithm, known as Electromagnetic Field Optimization (EFO). The proposed algorithm is inspired by the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, a possible solution is an electromagnetic particle made of electromagnets, and the number of electromagnets is determined by the number of variables of the optimization problem. EFO is a populationbased algorithm in which the population is divided into three fields (positive, negative, and neutral); attraction-repulsion forces among electromagnets of these three fields lead particles toward global minima. The golden ratio determines the ratio between attraction and repulsion forces to help particles converge quickly and effectively. The experimental results on 30 high dimensional CEC 2014 benchmarks reflect the superiority of EFO in terms of accuracy and convergence speed over other state-of-the-art optimization algorithms.

Novel Memetic Algorithm implemented With GA (Genetic Algorithm) and MADS (Mesh Adaptive Direct Search) for Optimal Design of Electromagnetic System

IEEE Transactions on Magnetics, 2010

This paper presents the novel implementation of the memetic algorithm with GA(Genetic Algorithm) and MADS(Mesh Adaptive Direct Search), which is applied for the optimal design methodology of the electric machine. This hybrid algorithm has been developed for obtaining the global optimum rapidly, which is effective for the optimal design of a electric machine with many local optima and much longer computation time. As a meta-heuristic search algorithm, MADS combined with a GA is validated with the Rastrigin function and the Shubert function with distinguished multimodal characteristics by investigating the evaluation number for optima convergence. In particular, the proposed algorithm has been forwarded to the optimal design of a direct-driven PM wind generator for maximizing the Annual Energy Production(AEP), of which design objective should be obtained by FEA(Finite Element Analysis). Finally, it is shown that MADS combined with GA has contributed to reducing the computation time effectively for the optimal design of a PM wind generator when compared with the purposely developed GA implemented with the parallel computing method. Index Terms-Finite element method (FEM), Genetic Algorithm (GA), Memetic Algorithm (MA), mesh adaptive direct search (MADS), surface-mounted permanent magnet synchronous generator (SPMSG).

Multi-Objective Optimization Using Evolution Strategies

Facta universitatis. Series electronics and energetics, 2009

The present paper gives an overview of different versions of Evolution Strategies, namely the (1+1) Evolution Strategy, the Higher Order (μ/ρ, λ ) Evolu- tion Strategy and the Niching (κ(μ/ρ, λ)) Evolution Strategy, and how these meth- ods 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.