An overview of population-based algorithms for multi-objective optimisation

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

Micromechanics of pyramidal indentation in fcc metals: Single crystal plasticity finite element analysis

International audienceThis work concerns analysis of Vickers and Berkovich indentation experiments through extensive crystal plasticity finite element simulations. The simulations are performed by recourse to the Bassani and Wu hardening model for pure fcc crystals undergoing both easy-glide stage I and stage II deformations, as well as with the model proposed by Pierce, Asaro and Needleman for precipitation hardened fcc crystals, deforming initially under stage II which also undergo strong hardening saturation in stage III. Simulations are also conducted with a model based on the hardening description by Bassani and Wu, whose physical basis and predictive capability have been enhanced for pure copper crystals. Based upon the activity of the slip systems and the strength of dislocation interactions, this work provides a fundamental insight into the influence of prior work hardening in single crystal indentation. Discussions are given on the role of the latent hardening description upon the development of material pileup and sinking-in at the contact boundary as well as on the correlation between the single crystal and polycrystalline contact responses. The present investigation further illustrates on the influence of the orientation of the slip systems with respect to the pyramidal tip upon the formation of irregular imprint morphologies. Extraction of the single crystal hardening parameters from instrumented indentation P–hs curves is also briefly addressed. Finally, the contact deformation regimes ruling the response of isotropic strain hardening media are examined in light of the simulations for single crystal indentation

Automatic Test Pattern Generation with BOA

We introduce a Bayesian Optimization algorithm (BOA) for the automatic generation of test sequences (ATPG) for digital circuit. We compare our approach, named BOATPG, to the two most known evolutionary approaches to ATPG (GATTO and STRATEGATE) and the currently most promising non-evolutionary approach to ATPG (namely, SPECTRAL ATPG). We show that our simple approach can easily outperform GATTO and performs as good as a more complex evolutionary approach like STRATEGATE. We also show that when BOATPG is coupled with spectral approach for seeding the population of initial test sequences, the resulting hybrid system, SBOATPG, performs better than the plain BOATPG although the improvements over SPECTRAL ATPG are limited

Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting

When a simple real-valued estimation of distribution algorithm (EDA) with Gaussian model and maximum likelihood estimation of parameters is used, it converges prematurely even on the slope of the fitness function. The simplest way of preventing premature convergence by multiplying the variance estimate by a constant factor k each generation is studied. Recent works have shown that when increasing the dimensionality of the search space, such an algorithm becomes very quickly unable to traverse the slope and focus to the optimum at the same time. In this paper it is shown that when isotropic distributions with Gaussian or Cauchy distributed norms are used, the simple constant setting of k is able to ensure a reasonable behaviour of the EDA on the slope and in the valley of the fitness function at the same time

Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift

Estimation-of-Distribution Algorithms (EDAs) are a specific type of Evolutionary Algorithm (EA). EDAs are characterized by the way in which new solutions are generated. The information in all selected solutions is combined at once. To this end, an interim representation that compresses and summarizes this information is used: a probability distribution over the solution space. New solutions are generated by sampling. Efficient optimization is guaranteed under suitable conditions. In practice it is however impossible to meet these conditions in general because arbitrarily complex distributions are required. Hence, practical techniques are required. In this paper, we focus on optimization of numerical functions using continuous distributions. The use of the normal distribution or combinations thereof is the most commonly adopted choice. It has already been so since the first EDAs in continuous spaces were introduced. An important question is how efficient EDAs are in the continuous domain using such practical distributions