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A variable neighbourhood search enhanced estimation of distribution algorithm for quadratic assignment problems

OPSEARCH 2021年第1期58卷 203-233页

作者： Pradeepmon, T. G. Panicker, Vinay V. Sridharan, R. Natl Inst Technol Calicut Dept Mech Engn Kozhikode Kerala India Muthoot Inst Technol & Sci Dept Mech Engn Ernakulam Kerala India

Quadratic Assignment Problem (QAP) is one of the most complex combinatorial optimization problems. Many real-world problems such as printed circuit board design, facility location problems, assigning gates to airplanes can be modelled as QAP. Problems of size greater than 35 is not able to solve optimally using conventional optimization methods. This warrants the use of evolutionary optimization methods for obtaining optimal or near optimal solutions for QAPs. This work proposes a hybridization on a univariate estimation of distribution Algorithm, namely the Population Based Incremental Learning Algorithm (PBILA), with Variable Neighbourhood Search (VNS) for solving QAPs. The proposed algorithm is employed to solve benchmark instances of QAP and the results are reported. The results of this work reveals that PBILA on its own is not efficient for solving the QAPs. However, when hybridised with VNS, the algorithm performs well providing best known solutions for 95 test instances out of the 101 instances considered. For most of the test instances, the percentage deviation is less than one percentage. The overall average percentage deviation of the obtained solutions from the best-known solutions is 0.037%, which is a significant improvement when compared with state-of-the-art algorithms.

关键词： Quadratic assignment problem estimation of distribution algorithms Variable neighbourhood search

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Inductive Logic Programming Through estimation of distribution Algorithm

Inductive Logic Programming Through Estimation of Distributi...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Pitangui, Cristiano Grijo Zaverucha, Gerson Univ Fed Rio de Janeiro Rio De Janeiro RJ Brazil

ISBN: (纸本)9781424478354

Genetic algorithms (GAs) are known for their capacity to explore large search spaces and due to this ability, they were to some extent applied to Inductive Logic Programming (ILP) problem. Although estimation of distribution algorithms (EDAs) perform better in most problems when compared to standard GAs, this kind of algorithm have not been applied to ILP. This work presents an ILP system based on EDA. Preliminary results show that the proposed system is superior when compared to a "standard" GA and it is very competitive when compared to the state of the art ILP system Aleph.

关键词： Inductive Logic Programming estimation of distribution algorithms Probabilistic Models

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A Novel Fuzzy Histogram based estimation of distribution Algorithm for Global Numerical Optimization

A Novel Fuzzy Histogram based Estimation of Distribution Alg...

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International Conference of Soft Computing and Pattern Recognition

作者： Liu, Weili Zhong, Jing-hui Wu, Wei-gang Xiao, Jing Zhang, Jun Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781424453306

Applying estimation of distribution algorithms (EDAs) to solve continuous problems is a significant and challenging task in the field of evolutionary computation. So far, various continuous EDAs have been developed based on different probability models. Initially, the EDAs based on a single Gaussian probability model are widely used but they have trouble in solving multimodal problems. Later EDAs based on a mixture model and on a clustering technique are then introduced to conquer such drawback. However, they are either time consuming or need prior knowledge of the problems. Recently, the histogram has begun to be used in continuous EDAs, but the histogram based EDAs (HEDAs) usually need too much time and space to gain a highly accurate solution. On the basis of pioneering contributions, this paper proposes a fuzzy histogram based EDA (FHEDA) for continuous optimization. In the FHEDA, the estimated range of the fuzzy histogram is adjusted adaptively by the current promising solutions, which leads the algorithm to search good solutions efficiently. A mutation mechanism is also introduced in the sampling operation to avoid being trapped in local optima. The performance of the proposed FHEDA is evaluated by testing seven benchmark functions with different characteristics. Two Gaussian based EDAs and the sur-shr-HEDA are studied for comparison. The results show that among all experimental algorithms, the FHEDA can give comparatively satisfying performance on unimodal and multimodal functions.

关键词： estimation of distribution algorithms Fuzzy Histogram Numerical Optimization Evolutionary algorithms

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A Layered Learning estimation of distribution Algorithm 22

A Layered Learning Estimation of Distribution Algorithm

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Li, Yong Yang, Qiang Gao, Xu-Dong Lu, Zhen-Yu Zhang, Jun Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan South Korea Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781450392686

Though estimation of distribution algorithms (EDAs) have witnessed success in problem optimization, most of them suffer from sharp shrink of covariance, which may lead to premature convergence. To alleviate this issue, this paper proposes a layered learning estimation of distribution algorithm (LLEDA) by maintaining multiple probability distribution models. Specifically, LLEDA first separates the population into several layers based on fitness. Then, the mean position of each layer is computed. Subsequently, we let the estimated mean position in each lower layer randomly learn from the one of a randomly selected higher layer, so that the mean positions of lower layers could be promoted to be close to promising areas found in the current population. At last, the covariance of each layer is estimated based on the generated new mean position and the individuals in this layer. By this means, multiple probability models with high quality are maintained and then are used to sample promising and diversified offspring separately. Comparative experiments conducted on a widely used benchmark problem set demonstrate that the proposed LLEDA achieves competitive or even much better performance than several state-of-the-art and representative EDAs.

关键词： estimation of distribution algorithms Multivariate Gaussian distribution Layered Learning Multiple distribution Models Global Numerical Optimization

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estimation of distribution Algorithm based on a Multivariate Extension of the Archimedean Copula

Estimation of Distribution Algorithm based on a Multivariate...

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1st BRICS Countries Congress on Computational Intelligence / 11th Brazilian Congress on Computational Intelligence (BRICS-CCI and CBIC)

作者： de Mello, Harold D., Jr. Abs da Cruz, Andre V. Vellasco, Marley M. B. R. Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro RJ Brazil

ISBN: (纸本)9781479931941

This paper presents a Copula-based estimation of distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism operators during the optimization. We show that this approach improves the overall performance of the optimization when compared to other copula-based EDAs.

关键词： continuous numeric optimization evolutionary computation estimation of distribution algorithms copulas

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An intelligent scatter with estimation of distribution for tabu search

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11th International Conference on Computer Aided Systems Theory

作者： Munetomo, Masaharu Satake, Yuta Akama, Kiyoshi Hokkaido Univ Informat Initiat Ctr Sapporo Hokkaido 0600811 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600811 Japan

ISBN: (纸本)9783540758662

This paper proposes an efficient optimization algorithm based on tabu search and estimation of distribution algorithms. The proposed algorithm estimates characteristics of distribution of solutions after performing tabu searches based on a marginal product model to acquire linkage information. Once correct linkage information is obtained, we can perform crossovers effectively without disrupting building blocks using the information. The proposed algorithm is expected to adapt to both global and local characteristics of solution space. Through empirical studies, we show the effectiveness of our approach.

关键词： tabu search extended compact genetic algorithm estimation of distribution algorithms

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estimation of distribution Algorithm using Factor Graph and Markov Blanket Canonical Factorization 14

Estimation of Distribution Algorithm using Factor Graph and ...

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16th Genetic and Evolutionary Computation Conference (GECCO)

作者： Helmi, B. Hoda Rahmani, Adel T. Iran Univ Sci & Technol Tehran *** Iran

ISBN: (纸本)9781450326629

Finding a good model and efficiently estimating the distribution is still an open challenge in estimation of distribution algorithms (EDAs). Factorization encoded by models in most of the EDAs are constrained. However for optimization of many real-world problems, finding the model capable of representing complex interactions without much computational complexity overhead is the key challenge. On the other hand factor graph which is the most natural graphical model for representing additively decomposable functions is rarely employed in EDAs. In this paper we introduce Factor Graph based EDA (FGEDA) which learns factor graph as the model and estimate the probability distribution represented by the learned factor graph using Markov blanket canonical factorization. The class of factorization that is employed for approximation of distribution in FGEDA is expanded relative to famous EDAs. We have used matrix factorization for learning the factor graph of the problem based on the pairwise mutual information between pair of variables. Gibbs sampling and BB- wise crossover are used to generate new samples. Empirical evaluation as well as theoretical analysis of the approach show the efficiency and power of FGEDA in the optimization of functions with complex interactions. It is showed experimentally that FGEDA outperform other well- known EDAs. GECCO track: estimation of distribution Algorithm.

关键词： Algorithm Design Experimentation Performance Optimization problems estimation of distribution algorithms Factor graph Linkage learning Mutual information Matrix factorization

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HSEDA: A Heuristic Selection Approach Based on estimation of distribution Algorithm for the Travelling Thief Problem 17

HSEDA: A Heuristic Selection Approach Based on Estimation of...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Martins, Marcella S. R. El Yafrani, Mohamed Delgado, Myriam R. B. S. Wagner, Markus Ahiod, Belaid Luders, Ricardo Fed Univ Technol Parana UTFPR Apucarana Brazil Mohammed V Univ Rabat Fac Sci LRIT URAC 29 Rabat Morocco Univ Adelaide Optimisat & Logist Adelaide SA Australia

ISBN: (纸本)9781450349208

Hyper-heuristics are high-level search techniques which improve the performance of heuristics operating at a higher heuristic level. Usually, these techniques automatically generate or select new simpler components based on the feedback received during the search. estimation of distribution algorithms (EDAs) have been applied as hyper-heuristics, using a probabilistic distribution model to extract and represent interactions between heuristics and its low-level components to provide high-valued problem solutions. In this paper, we consider an EDA-based hyper-heuristic framework which encompasses a Heuristic Selection approach aiming to find best combinations of different known heuristics. A surrogate assisted model evaluates the new heuristic combinations sampled by the EDA probabilistic model using an approximation function. We compare our proposed approach named Heuristic Selection based on estimation of distribution Algorithm (HSEDA) with three state-of-the-art algorithms for the Travelling Thief Problem (TTP). The experimental results show that the approach is competitive, outperforming the other algorithms on most of the medium-sized TTP instances considered in this paper.

关键词： Heuristic selection estimation of distribution algorithms Travelling Thief Problem Multi-component problems

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DAE-GP: Denoising Autoencoder LSTM Networks as Probabilistic Models in estimation of distribution Genetic Programming

DAE-GP: Denoising Autoencoder LSTM Networks as Probabilistic...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Wittenberg, David Rothlauf, Franz Schweim, Dirk Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (纸本)9781450371285

estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of the royal tree problem that DAE-GP outperforms standard GP and that performance differences increase with higher problem complexity. Furthermore, DAE-GP is able to create offspring with higher fitness from a learned model in comparison to standard GP. We believe that the key reason for the high performance of DAE-GP is that we do not impose any assumptions about the relationships between learned variables which is different to previous EDA-GP models. Instead, DAE-GP flexibly identifies and models relevant dependencies of promising candidate solutions.

关键词： Genetic Programming estimation of distribution algorithms Denoising Autoencoders Long Short-Term Memory Networks

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Introducing Generative adversarial networks on estimation of distribution algorithm to solve permutation-based problems

Introducing Generative adversarial networks on Estimation of...

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International Symposium on Innovative Informatics of Biskra (ISNIB)

作者： Lemtenneche, Sami Cheriet, Abdelhakim Bensayah, Abdallah Kasdi Merbah Univ Ouargla Algeria

ISBN: (纸本)9798350320657

As a subclass of evolutionary algorithms, estimation of distribution algorithms (EDAs) have found widespread use in a variety of optimization problems with impressive performance. In each generation, they build a probabilistic model representing the promising individuals, and the next generation constructs by sampling this model. Constructing and sampling the probabilistic model is a real challenge in developing such algorithms. Generative adversarial networks (GANs) are a popular kind of generative model that has been widely adopted due to their ability to generate new samples that closely match the distribution of training data. However, research on how GANs handle permutation spaces is lacking. We suggest a novel estimation of distribution Algorithm (EDA) that uses GANs as a probabilistic model estimator to advance this subject. in order to preserve the information captured from the selected individuals, those promising individuals were represented by a one-hot matrix, then used to train GANs. The proposed algorithm is tested to solve two permutation problems: The Travelling Salesman Problem (TSP) and Permutation Flowshop Scheduling Problem (PFSP). The Experimental results show that the proposed algorithm can obtain the optimal solution in some instances and a near-optimal in others.

关键词： Deep Generative Models Permutation Travelling Salesman Problem estimation of distribution algorithms

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