Soft computing and cooperative strategies for optimization

Cruz, Carlos; Pelta, David A.; Royo, Alejandro Sancho; Verdegay, José L.

doi:10.1016/j.asoc.2007.12.007

Cited by 27 publications

(2 citation statements)

References 17 publications

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“…In its simplest form, it stores the cost and the best solution seen so far in the system. This structure resembles the one developed by us in the context of cooperative systems or strategies for static problems in Pelta et al (2006) Cruz and Pelta (2008). We will come back to CIR later.…”

Section: Description Of the Proposalmentioning

confidence: 83%

Simple control rules in a cooperative system for dynamic optimisation problems

Pelta

Cruz

Verdegay

2009

International Journal of General Systems

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The use of centralised, multi-threads cooperative systems, has emerged as a successful alternative to deal with static optimisation problems, avoiding the problem of selecting a particular, isolated strategy. However, when the problem to deal with becomes dynamic in some sense, the question as to whether those systems and the type of control rules employed to control the threads are useful or not remains open.In this article, we depart from a strategy that joint uses a set of solutions and a set of simple agents, and we propose, compare and test two control rules for updating the former. The rules are a simple replacement frequency mechanism and a fuzzy set based one.Computational experiments are performed on the moving peaks benchmark problem under different scenarios and the main conclusions are: first, the fuzzy set based rule is better than the frequency based rule and second, both rules are competitive when compared with a state-of-the-art algorithm.

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Section: Description Of the Proposalmentioning

confidence: 83%

Simple control rules in a cooperative system for dynamic optimisation problems

Pelta

Cruz

Verdegay

2009

International Journal of General Systems

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“…In this interdisciplinary study, such a model is obtained using a combination of conventional and soft computing models. Soft computing (Cruz & Pelta, ; Corchado et al ., ; Torra & Narukawa, ) is a collection or set of computational techniques in machine learning (Abraham et al ., ), such as artificial neural networks (Kohonen, ; Herrero et al ., ), genetic algorithms (Lorena & de Carvalho, ; Naldi et al ., ), fuzzy systems (Zadeh, ; Berlanga et al ., ) and swarm intelligence (Das et al ., ), which investigate, simulate and analyse very complex issues and phenomena.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the operating conditions in a high precision industrial process using soft computing techniques

et al. 2011

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This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, copper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques.

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