Marina Tsili, Eleftherios I. Amoiralis, Jean Vianei Leite, Sinvaldo R. Moreno and Leandro dos Santos Coelho
Real-world applications in engineering and other fields usually involve simultaneous optimization of multiple objectives, which are generally non-commensurable and conflicting…
Abstract
Purpose
Real-world applications in engineering and other fields usually involve simultaneous optimization of multiple objectives, which are generally non-commensurable and conflicting with each other. This paper aims to treat the transformer design optimization (TDO) as a multiobjective problem (MOP), to minimize the manufacturing cost and the total owing cost, taking into consideration design constraints.
Design/methodology/approach
To deal with this optimization problem, a new method is proposed that combines the unrestricted population-size evolutionary multiobjective optimization algorithm (UPS-EMOA) with differential evolution, also applying lognormal distribution for tuning the scale factor and the beta distribution to adjust the crossover rate (UPS-DELFBC). The proposed UPS-DELFBC is useful to maintain the adequate diversity in the population and avoid the premature convergence during the generational cycle. Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.
Findings
Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.
Originality/value
This paper develops a promising UPS-DELFBC approach to solve MOPs. The TDO problems for three different transformer specifications, with 160, 400 and 630 kVA, have been addressed in this paper. Optimization results show the potential and efficiency of the UPS-DELFBC to solve multiobjective TDO and to produce multiple Pareto solutions.
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Leandro dos Santos Coelho, Viviana Cocco Mariani, Marsil de Athayde Costa e Silva, Nelson Jhoe Batistela and Jean Vianei Leite
The purpose of this paper is to introduce a chaotic harmony search (CHS) approach based on the chaotic Zaslavskii map to parameters identification of Jiles-Atherton vector…
Abstract
Purpose
The purpose of this paper is to introduce a chaotic harmony search (CHS) approach based on the chaotic Zaslavskii map to parameters identification of Jiles-Atherton vector hysteresis model.
Design/methodology/approach
In laminated magnetic cores when the magnetic flux rotates in the lamination plane, one observes an increase in the magnetic losses. The magnetization in these regions is very complex needing a vector model to analyze and predict its behavior. The vector Jiles-Atherton hysteresis model can be employed in rotational flux modeling. The vector Jiles-Atherton model needs a set of five parameters for each space direction taken into account. In this context, a significant amount of research has already been undertaken to investigate the application of metaheuristics in solving difficult engineering optimization problems. Harmony search (HS) is a derivative-free real parameter optimization metaheuristic algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. In this paper, a CHS approach based on the chaotic Zaslavskii map is proposed and evaluated.
Findings
The proposed CHS presents an efficient strategy to improve the search performance in preventing premature convergence to local minima when compared with the classical HS algorithm. Numerical comparisons with results using classical HS, genetic algorithms (GAs), particle swarm optimization (PSO), and evolution strategies (ES) demonstrated that the performance of the CHS is promising in parameters identification of Jiles-Atherton vector hysteresis model.
Originality/value
This paper presents an efficient CHS approach applied to parameters identification of Jiles-Atherton vector hysteresis model.
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Leandro dos Santos Coelho and Piergiorgio Alotto
This paper aims to show on a widely used benchmark problem that chaotic sequences can improve the search ability of evolution strategies (ES).
Abstract
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.
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Leandro dos Santos Coelho and Piergiorgio Alotto
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…
Abstract
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.
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Piergiorgio Alotto, Leandro dos Santos Coelho, Viviana C. Mariani and Camila da C. Oliveira
The purpose of this paper is to show with the help widely used analytical and application-oriented benchmark problems that a novel and relatively uncommon optimization method…
Abstract
Purpose
The purpose of this paper is to show with the help widely used analytical and application-oriented benchmark problems that a novel and relatively uncommon optimization method, lambda optimization, can be successfully applied to the solution of optimization problems in electromagnetics. Furthermore an improvement to the method is proposed and its effectiveness is validated.
Design/methodology/approach
An adaptive probability factor is used within the framework of lambda optimization.
Findings
It is shown that in the framework of lambda optimization (LO) the use of an adaptive probability factor can provide high-quality solutions with small standard deviation on the selected benchmark problem.
Research limitations/implications
Although the chosen benchmarks are 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 and validates the use of adaptive probability factor in order to improve the balance between the explorative and exploitative characteristics of the LO algorithm.
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Leandro dos Santos Coelho and Piergiorgio Alotto
The purpose of this paper is to show, on a widely used benchmark problem, that normative knowledge concepts can be incorporated into particle swarm optimization (PSO) algorithms…
Abstract
Purpose
The purpose of this paper is to show, on a widely used benchmark problem, that normative knowledge concepts can be incorporated into particle swarm optimization (PSO) algorithms in order to improve their search ability.
Design/methodology/approach
Normative knowledge concepts are used within the framework of PSO algorithms in order to influence the cognitive and social components of the particle behaviour.
Findings
It is shown that the proposed algorithm can significantly improve the performance of PSO on the selected benchmark problem, in terms of both best and average solutions.
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 normative knowledge concepts to control the cognitive and social components of PSO algorithms.
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Leandro Guarino Vasconcelos, Laercio Augusto Baldochi and Rafael Duarte Coelho Santos
This paper aims to presents Real-time Usage Mining (RUM), an approach that exploits the rich information provided by client logs to support the construction of adaptive Web…
Abstract
Purpose
This paper aims to presents Real-time Usage Mining (RUM), an approach that exploits the rich information provided by client logs to support the construction of adaptive Web applications. The main goal of RUM is to provide useful information about the behavior of users that are currently browsing a Web application. By consuming this information, the application is able to adapt its user interface in real-time to enhance the user experience. RUM provides two types of services as follows: support for the detection of struggling users; and user profiling based on the detection of behavior patterns.
Design/methodology/approach
RUM leverages the previous study on usability evaluation to provide a service that evaluates the usability of tasks performed by users while they browse applications. This evaluation is based on a metric that allows the detection of struggling users, making it possible to identify these users as soon as few logs from their interaction are processed. RUM also exploits log mining techniques to detect usage patterns, which are then associated with user profiles previously defined by the application specialist. After associating usage patterns to user profiles, RUM is able to classify users as they browse applications, allowing the application developer to tailor the user interface according to the users’ needs and preferences.
Findings
The proposed approach was exploited to improve user experience in real-world Web applications. Experiments showed that RUM was effective to provide support for struggling users to complete tasks. Moreover, it was also effective to detect usage patterns and associate them with user profiles.
Originality/value
Although the literature reports studies that explore client logs to support both the detection of struggling users and the user profiling based on usage patterns, no existing solutions provide support for detecting users from specific profiles or struggling users, in real-time, while they are browsing Web applications. RUM also provides a toolkit that allows the approach to be easily deployed in any Web application.
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Barbara de Lima Voss, David Bernard Carter and Bruno Meirelles Salotti
We present a critical literature review debating Brazilian research on social and environmental accounting (SEA). The aim of this study is to understand the role of politics in…
Abstract
We present a critical literature review debating Brazilian research on social and environmental accounting (SEA). The aim of this study is to understand the role of politics in the construction of hegemonies in SEA research in Brazil. In particular, we examine the role of hegemony in relation to the co-option of SEA literature and sustainability in the Brazilian context by the logic of development for economic growth in emerging economies. The methodological approach adopts a post-structural perspective that reflects Laclau and Mouffe’s discourse theory. The study employs a hermeneutical, rhetorical approach to understand and classify 352 Brazilian research articles on SEA. We employ Brown and Fraser’s (2006) categorizations of SEA literature to help in our analysis: the business case, the stakeholder–accountability approach, and the critical case. We argue that the business case is prominent in Brazilian studies. Second-stage analysis suggests that the major themes under discussion include measurement, consulting, and descriptive approach. We argue that these themes illustrate the degree of influence of the hegemonic politics relevant to emerging economics, as these themes predominantly concern economic growth and a capitalist context. This paper discusses trends and practices in the Brazilian literature on SEA and argues that the focus means that SEA avoids critical debates of the role of capitalist logics in an emerging economy concerning sustainability. We urge the Brazilian academy to understand the implications of its reifying agenda and engage, counter-hegemonically, in a social and political agenda beyond the hegemonic support of a particular set of capitalist interests.
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Erika Lisboa, Ricardo Corrêa Gomes and Humberto Falcão Martins