Search results for "Evolutionary algorithm"

Optimization of Delayed-State Kalman-Filter-based Algorithm via Differential Evolution for Sensorless Control of Induction Motors

2010

This paper proposes the employment of the differential evolution (DE) to offline optimize the covariance matrices of a new reduced delayed-state Kalman-filter (DSKF)-based algorithm which estimates the stator-flux linkage components, in the stationary reference frame, to realize sensorless control of induction motors (IMs). The DSKF-based algorithm uses the derivatives of the stator-flux components as mathematical model and the stator-voltage equations as observation model so that only a vector of four variables has to be offline optimized. Numerical results, carried out using a low-speed training test, show that the proposed DE-based approach is very promising and clearly outperforms a cla…

NASH EVOLUTIONARY ALGORITHMS: TESTING PROBLEM SIZE IN RECONSTRUCTION PROBLEMS IN FRAME STRUCTURES

2016

The use of evolutionary algorithms has been enhanced in recent years for solving real engineering problems, where the requirements of intense computational calculations are needed, especially when computational engineering simulations are involved (use of finite element method, boundary element method, etc). The coupling of game-theory concepts in evolutionary algorithms has been a recent line of research which could enhance the efficiency of the optimum design procedure and the quality of the design solutions achieved. They have been applied in several fields of engineering and sciences, mainly, in aeronautical and structural engineering (e.g: in computational fluid dynamics and solid mech…

Parallel global optimization : structuring populations in differential evolution

2010

Simple memetic computing structures for global optimization

2014

Soundscape design through evolutionary engines

2008

Abstract Two implementations of an Evolutionary Sound Synthesis method using the Interaural Time Difference (ITD) and psychoacoustic descriptors are presented here as a way to develop criteria for fitness evaluation. We also explore a relationship between adaptive sound evolution and three soundscape characteristics: keysounds, key-signals and sound-marks. Sonic Localization Field is defined using a sound attenuation factor and ITD azimuth angle, respectively (Ii, Li). These pairs are used to build Spatial Sound Genotypes (SSG) and they are extracted from a waveform population set. An explanation on how our model was initially written in MATLAB is followed by a recent Pure Data (Pd) impleme…

Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system

2017

We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and consists of three computationally expensive objective functions. We describe the modeling of the system and its numerical evaluation with a commercial software. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. Fr…

A Cooperative Coevolution Framework for Parallel Learning to Rank

2015

We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. E…

Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

2013

A new bi-objective genetic programming (BioGP) technique has been developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimizes training error through a single objective optimization procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat - a perennial problem in genetic …

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

2015

Classical evolutionary multi-objective optimization algorithms aim at finding an approx- imation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended preference-based evolutionary algorithm has been proposed for solving multi-objective optimiza- tion problems. Here, concepts from an interactive synchronous NIMBUS method are borrowed and combined with the R-NSGA-II algorithm. The proposed synchronous R-NSGA-II algorithm uses preference information provid…