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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.

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.

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.

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.

This paper aims to analyze a conceptual framework of supplier-manufacturer relationship in a lean supply chain environment, which considers two different configurations for the integration of information and material flows, aiming to better understand the applicability of such kind of approach to realistic cases.

Two different configurations for the integration of transport and material flows will be comparatively simulated and tested, aiming to better understand scientific implications and the applicability of such kind of approach to realistic cases in terms of performance of delivery service level and lead time.

The findings indicate that the conceptual model provides a framework to define threshold values of production variability to support the decision-making process regarding finished goods lean strategy. Furthermore, as the conceptual model considers as inputs the process variability of both supplier and customer’s production rates, it allows for the verification of the influence of supplier’s efficiency in the inventory sizing adopted in each case.

This study contributes to the body of literature on lean supply chain by proposing a simulation-based model that analyzes two different theoretical scenarios enabling the assessment of trade-offs among delivery service level, inventory strategy and production stability. This analysis provides theoretical arguments that may be extrapolated to real case situations, and considered other sources of instability that can impact the performance.

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.

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.

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.

This paper presents an efficient CHS approach applied to parameters identification of Jiles-Atherton vector hysteresis model.

This paper aims to show on a widely used benchmark problem that chaotic sequences can improve the search ability of evolution strategies (ES).

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.

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.

Although the benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

The proposed approach appears to be an efficient general purpose optimizer for electromagnetic design problems.

This paper introduces the use of chaotic sequences in the area of electromagnetic design optimization.

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.

An adaptive mutation factor and attractive/repulsive phases guided by population diversity are used within the framework of DE algorithms.

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.

Although the chosen benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.

This paper introduces the use of population diversity in order to guide the attractive/repulsive behavior of DE algorithms.

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.

An adaptive probability factor is used within the framework of lambda optimization.

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.

Although the chosen benchmarks are considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.

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.

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.

Normative knowledge concepts are used within the framework of PSO algorithms in order to influence the cognitive and social components of the particle behaviour.

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.

Although the chosen benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.

This paper introduces the use of normative knowledge concepts to control the cognitive and social components of PSO algorithms.

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.

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.

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.

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.