Search results for "Optimization algorithm"
showing 10 items of 51 documents
Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms
2016
We study how different types of preference information coming from a human decision maker can be utilized in an interactive multiobjective evolutionary optimization algorithm (MOEA). The idea is to convert different types of preference information into a unified format which can then be utilized in an interactive MOEA to guide the search towards the most preferred solution(s). The format chosen here is a set of reference vectors which is used within the interactive version of the reference vector guided evolutionary algorithm (RVEA). The proposed interactive RVEA is then applied to the multiple-disk clutch brake design problem with five objectives to demonstrate the potential of the idea in…
Optimization of net power density in Reverse Electrodialysis
2019
Abstract Reverse Electrodialysis (RED) extracts electrical energy from the salinity difference between two solutions using selective ion exchange membranes. In RED, conditions yielding a large net power density (NPD) are generally desired, due to the still large cost of the membranes. NPD depends on a large number of physical and geometric parameters. Some of these, for example the inlet concentrations of concentrate and diluate, can be regarded as “scenario” variables, imposed by external constraints (e.g., availability) or chosen by different criteria than NPD maximization. Others, namely the thicknesses HCONC, HDIL and the velocities UCONC, UDIL in the concentrate and diluate channels, c…
An optimized time screening algorithm for ROSAT PSPC and HRI observations
1998
We have developed a model-independent time screening optimization algorithm to cope with significant contamination spikes in the ROSAT PSPC/HRI bacground light-curves. The rejection criteria are based on the maximization of faint sources signal-to-noise ratio. The algorithm tuning parameters have been optimized through performing a wide set of runs on both simulated and real data. We have verified that the application of our selection criteria to the case of long exposure PSPC observations yields an increase of the number of faint sources ( SNR ) of up to 100% with a rejection of up to the 8% of the exposure time. At the same time, we obtain an average signal-to-noise ratio gain of 3% for t…
A predictive function optimization algorithm for multi-spectral skin lesion assessment
2015
The newly introduced Kubelka-Munk Genetic Algorithm (KMGA) is a promising technique used in the assessment of skin lesions. Unfortunately, this method is computationally expensive due to its function inverting process. In the work of this paper, we design a Predictive Function Optimization Algorithm in order to improve the efficiency of KMGA by speeding up its convergence rate. Using this approach, a High-Convergence-Rate KMGA (HCR-KMGA) is implemented onto multi-core processors and FPGA devices respectively. Furthermore, the implementations are optimized using parallel computing techniques. Intensive experiments demonstrate that HCR-KMGA can effectively accelerate KMGA method, while improv…
Online Closed-Loop Real-Time tES-fMRI for Brain Modulation: Feasibility, Noise/Safety and Pilot Study
2021
AbstractRecent studies suggest that transcranial electrical stimulation (tES) can be performed during functional magnetic resonance imaging (fMRI). The novel approach of using concurrent tES-fMRI to modulate and measure targeted brain activity/connectivity may provide unique insights into the causal interactions between the brain neural responses and psychiatric/neurologic signs and symptoms, and importantly, guide the development of new treatments. However, tES stimulation parameters to optimally influence the underlying brain activity in health and disorder may vary with respect to phase, frequency, intensity and electrode’s montage. Here, we delineate how a closed-loop tES-fMRI study of …
Integrated Production and Predictive Maintenance Planning based on Prognostic Information
2019
International audience; This paper address the problem of scheduling production and maintenance operation in predictive maintenance context. It proposes a contribution in the decision making phase of the prognostic and health management framework. Theprognostics and decision processes are merged and an ant colony optimization approach for finding the sequence of decisions that optimizes the benefits of a production system is developed. A case study on a single machine composed of several components where machine can have several usage profiles. The results show thatour approach surpasses classical condition based maintenance policy.
On the Use of Prognostics and Health Management to Jointly Schedule Production and Maintenance on a Single Multi-purpose Machine
2020
This paper address the problem of using prognostic information in the decision-making process of a single multi-purpose machine. The prognostics and health management method is compared to condition-based maintenance combined with a genetic algorithm to determine the joint schedule of maintenance and production. The paper presents a methodology to select the adequate strategy while considering several factors that influence the functioning of the machine. The results show that operational and conditions variability influence the choice of the suitable methods. In the presented case, we show configurations where prognostic information is useless or useful.
A Study on scale factor in distributed differential evolution.
2011
This paper proposes the employment of multiple scale factor values within distributed differential evolution structures. Four different scale factor schemes are proposed, tested, compared and analyzed. Two schemes simply employ multiple scale factor values and two also include an update logic during the evolution. The four schemes have been integrated for comparison within three recently proposed distributed differential evolution structures and tested on several various test problems. Numerical results show that, on average, the employment of multiple scale factors is beneficial since in most cases it leads to significant improvements in performance with respect to standard distributed alg…
Efficient evolutionary approach to approximate the Pareto-optimal set in multiobjective optimization, UPS-EMOA
2010
Solving real-life engineering problems requires often multiobjective, global, and efficient (in terms of objective function evaluations) treatment. In this study, we consider problems of this type by discussing some drawbacks of the current methods and then introduce a new population-based multiobjective optimization algorithm UPS-EMOA which produces a dense (not limited to the population size) approximation of the Pareto-optimal set in a computationally effective manner.
On PD mechanisms at high temperature in voids included in an epoxy resin
2001
In this paper the effects of temperature on partial discharge (PD) activity taking place inside a spherical void in epoxy resin system are studied. Indeed, some experimental tests previously performed on specimens, having different void shapes, under multi-stress condition of temperature and voltage, have shown very different PD amplitude distributions at temperatures higher than ambient. However, this phenomenon cannot be explained only by taking into account the different thermobaric conditions of the enclosed gas. In consequence of the general physical inaccessibility of such voids, a study is here performed using a numerical model based on an evolutionary optimization algorithm. This is…