Design Optimization of High-Frequency Power Transformer by Genetic Algorithm and Simulated Annealing

This paper highlights the transformer design optimization problem. The objective of transformer design optimization problem requires minimizing the total mass (or cost) of the core and wire material by satisfying constraints imposed by international standards and transformer user specification. The constraints include appropriate limits on efficiency, voltage regulation, temperature rise, no-load current and winding fill factor. The design optimizations seek a constrained minimum mass (or cost) solution by optimally setting the transformer geometry parameters and require magnetic properties. This paper shows the above design problems can be formulated in genetic algorithm(GA) and simulated annealing (SA) format. The importance of the GA and SA format stems for two main features. First it provides efficient and reliable solution for the design optimization problem with several variables. Second, it guaranteed that the obtained solution is global optimum. This paper includes a demonstration of the application of the GP technique to transformer design.Key word—Optimization, Power Transformer, Genetic Algorithm (GA), Simulated Annealing Technique (SA)DOI:http://dx.doi.org/10.11591/ijece.v1i2.8

Multiobjective genetic algorithm strategies for electricity production from generation IV nuclear technology

Development of a technico-economic optimization strategy of cogeneration systems of electricity/hydrogen, consists in finding an optimal efficiency of the generating cycle and heat delivery system, maximizing the energy production and minimizing the production costs. The first part of the paper is related to the development of a multiobjective optimization library (MULTIGEN) to tackle all types of problems arising from cogeneration. After a literature review for identifying the most efficient methods, the MULTIGEN library is described, and the innovative points are listed. A new stopping criterion, based on the stagnation of the Pareto front, may lead to significant decrease of computational times, particularly in the case of problems involving only integer variables. Two practical examples are presented in the last section. The former is devoted to a bicriteria optimization of both exergy destruction and total cost of the plant, for a generating cycle coupled with a Very High Temperature Reactor (VHTR). The second example consists in designing the heat exchanger of the generating turbomachine. Three criteria are optimized: the exchange surface, the exergy destruction and the number of exchange modules

A hybrid CFGTSA based approach for scheduling problem: a case study of an automobile industry

In the global competitive world swift, reliable and cost effective production subject to uncertain situations, through an appropriate management of the available resources, has turned out to be the necessity for surviving in the market. This inspired the development of the more efficient and robust methods to counteract the existing complexities prevailing in the market. The present paper proposes a hybrid CFGTSA algorithm inheriting the salient features of GA, TS, SA, and chaotic theory to solve the complex scheduling problems commonly faced by most of the manufacturing industries. The proposed CFGTSA algorithm has been tested on a scheduling problem of an automobile industry, and its efficacy has been shown by comparing the results with GA, SA, TS, GTS, and hybrid TSA algorithms

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners