MOPED: A Multi-objective Parzen-Based Estimation of Distribution Algorithm for Continuous Problems

Costa, M.; Minisci, Edmondo

doi:10.1007/3-540-36970-8_20

Cited by 55 publications

(61 citation statements)

References 9 publications

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“…The literature [22] used local PCA to do clustering analysis, although it can more accurately describe the distribution rule of solution set. Its computational complexity is very expensive.…”

Section: K-means Clustering Analysis [21]mentioning

confidence: 99%

A Multi-objective Evolutionary Algorithm of Principal Curve Model Based on Clustering Analysis

Qin¹,

Dai²

2016

JSW

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According to the traditional GA and EDA weakness, on the basis of MMEA, the orthogonal design initialization, convergence criterion and K-means clustering analysis method were introduced in this paper and it proposed a new model multi-objective evolutionary algorithm OMEA. The practice results showed that the OMEA had been greatly improved on both convergence and diversity of the solutions, reaching a good balance on diversity and convergence. Its comprehensive performance was better than the SPEA2, NSGA-II and other traditional multi-objective evolutionary algorithm.

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Section: K-means Clustering Analysis [21]mentioning

confidence: 99%

A Multi-objective Evolutionary Algorithm of Principal Curve Model Based on Clustering Analysis

Qin¹,

Dai²

2016

JSW

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“…They also employed a more complicated mixture of variational Bayesian independent component analyzers in a later study (Cho and Zhang 2004). MOPEDA (Costa and Minisci 2003) applies a Parzen estimator that convolves the empirical estimation obtained from a finite data set with a squared integrate kernel function in order to reduce the variance of the probability distribution estimation. Both Gaussian and Cauchy kernels are used alternatively during evolution to utilize their intrinsic complementary characteristics.…”

Section: Non-parametric Probabilistic Modelsmentioning

confidence: 99%

A review on probabilistic graphical models in evolutionary computation

et al. 2012

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Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

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“…The MOPED (Multi-Objective Parzen based Estimation of Distribution) algorithm is a multi-objective optimisation algorithm for continuous problems that uses the Parzen method to build a probabilistic representation of Pareto solutions, with multivariate dependencies among variables [4], [2]. Similarly to what was done in [8] for multi-objective Bayesian Optimisation Algorithm (moBOA), some techniques of NSGA-II are used to classify promising solutions in the objective space, while new individuals are obtained by sampling from the Parzen model.…”

Section: A the Evolutionary Optimisation Algorithmmentioning

confidence: 99%

“…The Parzen method [4] pursues a non-parametric approach to kernel density estimation and it gives rise to an estimator that converges everywhere to the true Probability Density Function (PDF) in the mean square sense. Should the true PDF be uniformly continuous, the Parzen estimator can also be made uniformly consistent.…”

Section: A the Evolutionary Optimisation Algorithmmentioning

confidence: 99%

Evolutionary design of a full-envelope flight control system for an unstable fighter aircraft

Avanzini

Minisci

2010

IEEE Congress on Evolutionary Computation

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This version is available at https://strathprints.strath.ac.uk/41393/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Evolutionary Design of a Full-Envelope Flight Control System for an Unstable Fighter AircraftGiulio Avanzini, and Edmondo A. MinisciAbstract-The use of an evolutionary algorithm in the framework of H ∞ control theory is being considered as a means for synthesizing controller gains that minimize a weighted combination of the infinite-norm of the sensitivity function (for disturbance attenuation requirements) and complementary sensitivity function (for robust stability requirements) at the same time. The case study deals with the stability and control augmentation of an unstable high-performance jet aircraft. Constraints on closed-loop response are also enforced, that represent typical requirements on airplane handling qualities, that makes the control law synthesis process more demanding.Gain scheduling is required, in order to obtain satisfactory performance over the whole flight envelope, so that the synthesis is performed at different reference trim conditions, for several values of the dynamic pressure, Q, used as the scheduling parameter. Nonetheless, the dynamic behaviour of the aircraft may exhibit significant variations when flying at different altitudes h, even for the same value of the dynamic pressure, so that a trade-off is required between different feasible controllers synthesized for a given value of Q, but different h. A multiobjective search is thus considered for the determination of the best suited solution to be introduced in the scheduling of the control law. The obtained results are then tested on a longitudinal nonlinear model of the aircraft.

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MOPED: A Multi-objective Parzen-Based Estimation of Distribution Algorithm for Continuous Problems

Cited by 55 publications

References 9 publications

A Multi-objective Evolutionary Algorithm of Principal Curve Model Based on Clustering Analysis

A Multi-objective Evolutionary Algorithm of Principal Curve Model Based on Clustering Analysis

A review on probabilistic graphical models in evolutionary computation

Evolutionary design of a full-envelope flight control system for an unstable fighter aircraft

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