Search results for "Evolutionary algorithm"
showing 10 items of 119 documents
Spectral phase reconstruction of femtosecond laser pulse from interferometric autocorrelation and evolutionary algorithm
2021
International audience; We report on the complete temporal characterization of femtosecond laser pulses from second-order interferometric autocorrelation and laser spectrum measurements. The method exploits a newly developed autocorrelator based on a two photon-absorption signal produced directly within a camera sensor so as to provide a single-shot interferometric autocorrelation of great reliability and robustness. Interferometric autocorrelation trace and laser spectrum are exploited for a spectral phase retrieval via an evolutionary algorithm. The quality of the reconstruction for highly modulated spectral phases imprinted by a pulse shaper confirms the reliability of the method. The au…
Evolutionary approach to coverage testing of IEC 61499 function block applications
2015
The paper addresses the problem of coverage testing of industrial automation software represented in the IEC 61499 standard, one of the recent standards for distributed control system design. Contrary to model-based testing (MBT), the paper focuses on implementation coverage, not model coverage. An approach based on evolutionary algorithms is presented which generates coverage test suites for both basic and composite IEC 61499 function blocks. It employs two third-party tools, FBDK and EvoSuite. The evaluation of the approach was performed on a set of control applications for two lab-scale demonstration plants. Results show that the approach is applicable and shows good performance at least…
Data-Driven Evolutionary Optimization: An Overview and Case Studies
2019
Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist, instead computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this…
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem
2017
A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process. peerReviewed
Considering safety issues in minimum losses reconfiguration for MV distribution networks
2009
This paper offers a new perspective over the traditional problem of the multiobjective optimal reconfiguration of electrical distribution systems in regular working state. The issue is indeed here formulated including also safety issues. Indeed, dimensioning the earth electrodes of their own secondary substations, distribution companies take into account the probable future configurations of the network due to transformations of overhead lines into cable lines or realization of new lines. On the contrary, they do not consider that, during normal working conditions. the structure of the network can be modified for long periods as a consequence of reconfiguration manoeuvres, with differences …
On sampling error in evolutionary algorithms
2021
The initial population in evolutionary algorithms (EAs) should form a representative sample of all possible solutions (the search space). While large populations accurately approximate the distribution of possible solutions, small populations tend to incorporate a sampling error. A low sampling error at initialization is necessary (but not sufficient) for a reliable search since a low sampling error reduces the overall random variations in a random sample. For this reason, we have recently presented a model to determine a minimum initial population size so that the sampling error is lower than a threshold, given a confidence level. Our model allows practitioners of, for example, genetic pro…
Optimizing the Integration Area and Performance of VLIW Architectures by Hardware/Software Co-design
2021
The cost and the performance are major concerns that the designers of embedded processors shall take into account, especially for market considerations. In order to reduce the cost, embedded systems rely on simple hardware architectures like VLIW (Very Long Instruction Word) processors and they look for compiler support. This paper aims at developing a design space explorer of VLIW architectures from different perspectives like processing performance and integration area. A multi-objective Genetic Algorithm (GA) was used to find the optimum hardware configuration of an embedded system and the optimization rules applied by compiler on the benchmarks code. The first step consisted in represen…
Multi-modal search for multiobjective optimization: an application to optimal smart grids management
2012
This paper studies the possibility to use efficient multimodal optimizers for multi-objective optimization. In this paper, the application area considered for such new approach is the optimal dispatch of energy sources in smart microgrids. The problem indeed shows a non uniform Pareto front and requires efficient optimal search methods. The idea is to exploit the potential of agents in population-based heuristics to improve diversity in the Pareto front, where solutions show the same rank and are thus equally weighted. Since Pareto dominance is at the basis of the theory of multi-objective optimization, most algorithms show the non dominance ranking as quality indicator, with some problem i…
Connections with Other Population-Based Approaches
2003
Throughout this book, we have established that scatter search (SS) belongs to the family of population-based metaheuristics. This family also includes the well-known evolutionary algorithms and the approach known as path relinking.