Rodrigo C. P. Silva

A Computationally Efficient Algorithm for Rotor Design Optimization of Synchronous Reluctance Machines

A generalizable algorithm is proposed for the design optimization of synchronous reluctance machine rotors. Single-barrier models are considered to reduce the algorithms computational complexity and provide a relative comparison for rotors with different slots-per-pole combinations. Two objective values per sampled design (average and ripple torques) are computed using 2-D finite-element analysis simulations. Non-linear regression or surrogate models are trained for the two objectives through a Bayesian regularization backpropagation neural network. A multi-objective genetic algorithm is used to find the validated Pareto front solutions. An analytical ellipse constraint is then suggested to encapsulate optimal solutions. Compared with a direct sampling approach, this restriction captures an optimal region within the double-barrier space for further torque ripple reduction.

Visualization and Analysis of Tradeoffs in Many-Objective Optimization: A Case Study on the Interior Permanent Magnet Motor Design

The presentation and visualization of tradeoff solutions in many-objective optimization problems are difficult due to the large number of solutions in a hyperdimensional objective space. A recently proposed tool, known as aggregation tree (AT), can be used to analyze the degree of conflict between groups of objectives in a many-objective problem. In this paper, we present a case study on the internal permanent magnet motor design with seven objectives. The results show that the AT provides useful information about objective relationships (in accordance with the common knowledge of physics) as well as guidance in the reduction of objectives.

A New Robust Dominance Criterion for Multiobjective Optimization

This paper discusses robust design issues in the multiobjective design of electromagnetic devices. A Pareto front of the worst cases of the solution due to the perturbation of the design variables is used to describe the robustness of a design. A robust dominance (r-dominance) relationship between two solutions of a multiobjective problem is defined. To reduce the computational cost of evaluating the robustness, a local response surface model is built on the uncertainty set. This robust multiobjective design scheme was applied to the design of an interior permanent magnet machine, where a tradeoff exists between the average torque and the torque ripple as the rotor angle is varied. The results show that the proposed r-dominance, when embedded in a multiobjective optimization search method, is able to find robust solutions to multiobjective design problems.

Hybrid self-adaptive evolution strategies guided by neighborhood structures for combinatorial optimization problems

This article presents an Evolution Strategy (ES)--based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration–exploitation balance. To validate the proposal, this framework is applied to solve three different -Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.

Design and optimization of fractional slot concentrated winding permanent magnet machines for class IV electric vehicles

The design, optimization and application of a number of surface mounted fractional slot concentrated winding (FSCW) electric machines for application to Class IV electric vehicles have been considered. Four FSCW motors with nominal power ratings of 50, 65, 75 and 100 kW have been designed. The motors were optimized using a novel multi-objective optimization strategy which allows a large numbers of objectives to be considered while ensuring computational efficiency and Pareto optimality. Vehicle simulations were carried out using the optimized motors for some typical drive cycles. The gear ratio of the drive train was optimized for each motor with respect to the drive cycle and the vehicle performances were calculated. The methodology and results presented provide a novel and improved framework for considering the trade-offs between the motor size, gear ratio and vehicle performance for Class IV and other vehicle classes.

A study on the configuration of migratory flows in island model differential evolution

The Island Model (IM) is a well known multi-population approach for Evolutionary Algorithms (EAs). One of the critical parameters for defining a suitable IM is the migration topology. Basically it determines the Migratory Flows (MF) between the islands of the model which are able to improve the rate and pace of convergence observed in the EAs coupled with IMs. Although, it is possible to find a wide number of approaches for the configuration of MFs, there still is a lack of knowledge about the real performance of these approaches in the IM. In order to fill this gap, this paper presents a thorough experimental analysis of the approaches coupled with the state-of-the-art EA Differential Evolution. The experiments on well known benchmark functions show that there is a trade-off between convergence speed and convergence rate among the different approaches. With respect to the computational times, the results indicate that the increase in implementation complexity does not necessarily represent an increase in the overall execution time.

Dynamic selection of migration flows in island model differential evolution

In this paper, a new approach to the topology configuration problem in the Island Model (IM) is proposed. The mechanism proposed works with a pool of candidates for migration and the choice of immigrants is performed using the usual selection techniques of evolutionary algorithms. Computational tests on IM versions of the Differential Evolution show positive effects of the proposed approach in terms of the number of function evaluations required for convergence.

On the visualization of trade-offs and reducibility in many-objective optimization

This paper proposes a technique of Aggregation Trees to visualize the results of high-dimensional multiobjective optimization problems, or many-objective problems. The high-dimensionality makes it difficult to represent the relation between objectives and solutions. Most approaches in the literature are based on the representation of solutions in lower dimensions. The technique of Aggregation Trees proposed here is based on iterative aggregation of objectives which are represented in a tree. Besides, the location of conflict is also calculated and represented on the tree. Thus, the tree can represent which objectives and groups of objectives are harmonic the most, what sort of conflict is present between groups of objectives, and which aggregations would be more interesting in order to reduce the problem dimension.

Surrogate-based MOEA/D for electric motor design with scarce function evaluations

This paper proposes a surrogate-assisted multiobjective evolutionary algorithm based on decomposition (sMOEA/D) for the design of electric motors. The idea is to improve the surrogate gradually during the optimization. Simulation results show that the proposed method is competitive with state-of-the-art multiobjective optimization algorithms needing only a small number of function evaluations.

Global and local meta-models for the robust design of electrical machines

This paper presents an optimization framework for the robust design of electrical machines. During the run of a genetic algorithm, all evaluated solutions are kept in an archive, and are used to build different meta-models for the assessment of robustness. The proposed robust design algorithms are verified using analytical test functions and are shown to be efficient and accurate. They have also been applied to the robust design of a surface mount permanent magnet machine considering manufacturing imprecision and different optimal designs (robust and non-robust) are obtained.