Search results for "Evolutionary algorithm"
showing 10 items of 119 documents
Evolutionary algorithm for fiber laser in ultrashort pulse regime
2016
This thesis deals with the generation of ultrashort pulses within a fiber laser cavity through the automatic optimization of its parameters by an evolutionary algorithm. The interest of this subject comes from the difficulty to systematically explore dynamics in a large domain of experimental parameters. We have shown that it is possible to implement an evolutionary algorithm on fiber laser cavity with appropriate precautions. We have experimentally demonstrated for the first time the mode locking of a laser cavity only using the optimization of polarization controllers through an automatic and self-learning procedure. We also have demonstrated that selecting the mode locking from it radio-…
An enhanced memetic differential evolution in filter design for defect detection in paper production.
2008
This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adap…
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies
2018
We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary a…
A New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution
2011
Differential evolution has become one of the most widely used evolution- ary algorithms in multiobjective optimization. Its linear mutation operator is a sim- ple and powerful mechanism to generate trial vectors. However, the performance of the mutation operator can be improved by including a nonlinear part. In this pa- per, we propose a new hybrid mutation operator consisting of a polynomial based operator with nonlinear curve tracking capabilities and the differential evolution’s original mutation operator, to be efficiently able to handle various interdependencies between decision variables. The resulting hybrid operator is straightforward to implement and can be used within most evoluti…
A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization
2018
We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distr…
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm
2019
We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. We are motivated by practical applicability and focus on two main challenges faced by practitioners in industry: 1) meaningful formulation of the optimization problem reflecting the needs of a decision maker and 2) finding a desirable solution based on a decision maker’s preferences when solving a problem with computationally expensive function evaluations. For the first challenge, we describe the procedure of modelling a component in the air intake ventilation system wi…
Benchmarking parameter-free AMaLGaM on functions with and without noise.
2013
We describe a parameter-free estimation-of-distribution algorithm (EDA) called the adapted maximum-likelihood Gaussian model iterated density-estimation evolutionary algorithm (AMaLGaM-ID[Formula: see text]A, or AMaLGaM for short) for numerical optimization. AMaLGaM is benchmarked within the 2009 black box optimization benchmarking (BBOB) framework and compared to a variant with incremental model building (iAMaLGaM). We study the implications of factorizing the covariance matrix in the Gaussian distribution, to use only a few or no covariances. Further, AMaLGaM and iAMaLGaM are also evaluated on the noisy BBOB problems and we assess how well multiple evaluations per solution can average ou…
Looking for the best modes helps solving the MRCPSP/max
2013
The multi-mode resource-constrained project scheduling problem with minimum and maximum time lags MRCPSP/max is a very general project scheduling problem with multiple execution modes per activity, renewable and non-renewable resources and minimum and maximum time lags between activities. In this paper, we describe SA-EVA, an algorithm for the problem. SA-EVA first searches for the best mode for each activity, without considering renewable resources. In this phase a simulated annealing is applied. Once a mode vector has been chosen, the problem reduces to the RCPSP/max, which SA-EVA solves with EVA, an algorithm designed in Ballestin et al. [2009. An evolutionary algorithm for the resource-…
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…
A memetic approach to discrete tomography from noisy projections
2010
Discrete tomography deals with the reconstruction of images from very few projections, which is, in the general case, an NP-hard problem. This paper describes a new memetic reconstruction algorithm. It generates a set of initial images by network flows, related to two of the input projections, and lets them evolve towards a possible solution, by using crossover and mutation. Switch and compactness operators improve the quality of the reconstructed images during each generation, while the selection of the best images addresses the evolution to an optimal result. One of the most important issues in discrete tomography is known as the stability problem and it is tackled here, in the case of no…