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15th IEEE International Conference on High Performance Computing and Communications (HPCC) /11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC)

作者： Wang, Yaqing Wang, Hua Kong, Guohong Shandong Univ Sch Comp Sci & Technol Jinan 250100 Shandong Peoples R China

ISBN: (纸本)9780769550886

As one of the most well-known combinatorial optimization problems, Steiner tree problem is widely applied to optimization in transportation design, biological engineering, and communication networks. It has been proved to be NP complete, though. To solve this problem, researchers have provided many classic solutions. This paper proposes a new method of solving Steiner tree problem by using estimation of distribution algorithms (EDA). The basic idea is to initialize randomly n trees which contain the source node and the destination nodes. Some elites are selected by the selection operator. The algorithm then constructs a probabilistic model which attempts to estimate the probability distribution of the selected elites. Once the model is constructed, new trees are generated by sampling the distribution encoded by this model. These new trees are then incorporated back into the old population, possibly replacing it entirely. The process is repeated until some termination criteria are met. The algorithm constantly evolves trees to obtain a better solution tree with EDA ideas. This method leads to better performance, reduced time complexity, and optimized solution. Simulation results also show that the algorithm has better performance in searching and converging.

关键词： Steiner Tree estimation distribution algorithm approximation algorithm intelligence optimization probabilistic model

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A Dual Center Approach to CMA-ES

A Dual Center Approach to CMA-ES

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作者： Bourne, Dillon University of Guelph

学位级别：硕士

Covariance Matrix Adaptation - Evolution Strategies (CMA-ES) is a renowned state-of-the-art black-box optimzation algorithm in the field of Evolutionary Computation. As real-world optimzation problems began to be characterized as multimodal, CMA-ES was subject to modifications to improve it’s performance on these problems. The rise of multimodal problems presented a new challenge for optimzation algorithms which is avoiding local optima while trying to find the global optimum. The CMA-ES algorithm, although powerful, is not guaranteed to beat this challenge as it may be sampling in an area which contains basins of attraction where the global optimum does not reside. This could be attributed to the fact that CMA-ES uses a single-model estimation distribution algorithm (EDA) to determine a single point in the problem landscape from which to perform sampling. This research investigates the performance of CMA-ES on several multimodal and unimodal problems using two different EDAs with an overlapping model to perform sampling within the problem landscape. This proposed system, Dualcenter-CMA-ES (DC-CMA-ES) outperforms IPOP-CMA-ES on complex multimodal functions, especially as problem dimensionality increases.

关键词： CMA-ES estimation distribution algorithm EDA Optimization BBOB Elitism History Age Multimodal Synchronized Sampling

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Energy management for plug-in hybrid electric vehicles considering optimal engine ON/OFF control and fast state-of-charge trajectory planning

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ENERGY 2018年 163卷 457-474页

作者： Guo, Ningyuan Shen, Jiangwei Xiao, Renxin Yan, Wensheng Chen, Zheng Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650500 Yunnan Peoples R China

In this paper, a novel energy management strategy for plug-in hybrid electric vehicles (PHEVs) is proposed, considering engine on/off control and state-of-charge (SOC) trajectory planning. First, the powertrain structure is analyzed in detail, and then the fuel rate is fitted versus the battery power by a series of quadratic equations. Pontryagin's minimum principle is employed to determine the optimal engine on/off switching and battery power commands, and the estimation distribution algorithm is applied to search for the optimal equivalent factor. Numerical simulations and hardware-in-the-loop experiments validate that near-optimal fuel performance can be achieved based on the proposed algorithm with reasonable computational cost. In addition, the SOC reference trajectory optimized by the proposed algorithm is validated to be effective for application with online energy management strategies. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Engine on/off control estimation distribution algorithm Fast state-of-charge trajectory planning Pontryagin's minimum principle

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EDA based Probabilistic Memetic algorithm for Distributed Blocking Permutation Flowshop Scheduling with Sequence Dependent Setup Time

EDA based Probabilistic Memetic Algorithm for Distributed Bl...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Duan, Wenzhe Li, Zhengyang Yang, Yixin Liu, Bo Wang, Keyao Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Beihang Univ Astronaut Sch Beijing 100191 Peoples R China

ISBN: (纸本)9781509046010

Distributed permutation flowshop scheduling problem (DPFSP) is a typical NP-hard combinatorial optimization problem and represents an important area in multiple distributed production systems. In this study, both machine blocking and job sequence dependent setup time constraints are considered in DPFSP, which make the conventional model more suitable to the realistic situation. The combination with new constraints means a substantial increase in the complexity of the problem and the volatility of landscape, which sharply increase the solving difficulty. Probabilistic memetic framework (PrMF) is a novel MA framework which balances the exploration and exploitation by controlling the learning intensity of each individual. In this paper, an EDA-based PrMF algorithm, called EDAPrMF, is proposed to solve the DBPFSP with SDST, in which PrMF is modified and extended for distributed scheduling problems with two-layer encoding. Specifically, a novel solution matrix based distance matrix is defined for DBPFSP with SDST, which serves as a suitable measure between two feasible solutions and can also be connected with the probability matrix of EDA, the global search meme in PrMF. Meta-Lamarckian learning strategy is also equipped in PrMF to guide the local search direction. The experimental results and comparisons with existing algorithms show the efficiency of the proposed EDAPrMF in solving both small-scale and large-scale DBPFSP with SDSP and the effectiveness of PrMF in improve the search ability.

关键词： distributed blocking permutation flowshop scheduling problem distributed permutation flowshop scheduling problem sequence dependent setup time probabilistic memetic algorithm estimation distribution algorithm

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Evolutionary optimization of sparsely connected and time-lagged neural networks for time series forecasting

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APPLIED SOFT COMPUTING 2014年 23卷 432-443页

作者： Peralta Donate, Juan Cortez, Paulo Univ Autonoma Barcelona E-08193 Barcelona Spain Univ Minho ALGORITMI Res Ctr Dept Informat Syst P-4800058 Guimaraes Portugal

Time series forecasting (TSF) is an important tool to support decision making (e.g., planning production resources). Artificial neural networks (ANNs) are innate candidates for TSF due to advantages such as nonlinear learning and noise tolerance. However, the search for the best model is a complex task that highly affects the forecasting performance. In this work, we propose two novel evolutionary artificial neural networks (EANNs) approaches for TSF based on an estimation distribution algorithm (EDA) search engine. The first new approach consist of sparsely connected evolutionary ANN (SEANN), which evolves more flexible ANN structures to perform multi-step ahead forecasts. The second one, consists of an automatic Time lag feature selection EANN (TEANN) approach that evolves not only ANN parameters (e.g., input and hidden nodes, training parameters) but also which set of time lags are fed into the forecasting model. Several experiments were held, using a set of six time series, from different real-world domains. Also, two error metrics (i.e., mean squared error and symmetric mean absolute percentage error) were analyzed. The two EANN approaches were compared against a base EANN (with no ANN structure or time lag optimization) and four other methods (autoregressive integrated moving average method, random forest, echo state network and support vector machine). Overall, the proposed SEANN and TEANN methods obtained the best forecasting results. Moreover, they favor simpler neural network models, thus requiring less computational effort when compared with the base EANN. (C) 2014 Elsevier B.V. All rights reserved.

关键词： estimation distribution algorithm Multilayer perceptron Time series Regression

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Self-optimising intelligent distributed antenna system for geographic load balancing

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IET COMMUNICATIONS 2014年第15期8卷 2751-2761页

作者： Hejazi, Seyed Amin Stapleton, Shawn P. Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

Increase in number of mobile users, generates unbalanced load traffic in wireless network. In this study, a load-balancing solution is investigated in order to optimise quality of service. An intelligent distributed antenna system (IDAS) fed by a base transceiver station (BTS) has the ability to distribute the cellular capacity over a given geographic area depending on the time-varying traffic. A virtual cell network is an IDAS with capacity routing capability. To enable load balancing among distributed antenna modules, the authors dynamically allocate the remote antenna modules to the BTS sectors. A self-organised network of virtual cells is formulated as an optimisation problem, which attempts to balance traffic load and minimises the hand-offs as two important cost factors in the network. Two evolutionary algorithms are proposed for optimisation: genetic algorithm and estimation distribution algorithm. Computational results of different traffic scenarios after performing the algorithms, demonstrate that the two algorithms attain excellent key performance indicators for small-scale networks.

关键词： antenna arrays genetic algorithms cellular radio Long Term Evolution self-optimising intelligent distributed antenna system geographic load balancing unbalanced load traffic IDAS base transceiver station BTS cellular capacity virtual cell network remote antenna modules optimisation problem evolutionary algorithms genetic algorithm estimation distribution algorithm LTE DAS network

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Global and decomposition evolutionary support vector machine approaches for time series forecasting

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NEURAL COMPUTING & APPLICATIONS 2014年第5期25卷 1053-1062页

作者： Cortez, Paulo Peralta Donate, Juan Univ Minho Dept Informat Syst ALGORITMI Res Ctr P-4800058 Guimaraes Portugal Univ Autonoma Barcelona E-08193 Barcelona Spain

Multi-step ahead time series forecasting (TSF) is a key tool for supporting tactical decisions (e.g., planning resources). Recently, the support vector machine (SVM) emerged as a natural solution for TSF due to its nonlinear learning capabilities. This paper presents two novel evolutionary SVM (ESVM) methods for multi-step TSF. Both methods are based on an estimation distribution algorithm search engine that automatically performs a simultaneous variable (number of inputs) and model (hyperparameters) selection. The global ESVM (GESVM) uses all past patterns to fit the SVM, while the decomposition ESVM (DESVM) separates the series into trended and stationary effects, using a distinct ESVM to forecast each effect and then summing both predictions into a single response. Several experiments were held, using six time series. The proposed approaches were analyzed under two criteria and compared against a recent evolutionary artificial neural network (EANN) and two classical forecasting methods, Holt-Winters and autoregressive integrated moving average. Overall, the DESVM and GESVM obtained competitive and high-quality results. Furthermore, both ESVM approaches consume much less computational effort when compared with EANN.

关键词： estimation distribution algorithm Support vector machines Time series Decomposition forecasting Model selection

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Evolution of Developmental Timing for Solving Hierarchically Dependent Deceptive Problems

Evolution of Developmental Timing for Solving Hierarchically...

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10th International Conference on Simulated Evolution and Learning (SEAL)

作者： Hamano, Kouta Ohnishi, Kei Koeppen, Mario Kyushu Inst Technol Iizuka Fukuoka 8208502 Japan

ISBN: (纸本)9783319135632;9783319135625

Conventional evolutionary algorithms (EAs) cannot solve given optimization problems efficiently when their evolutionary operators do not accommodate to the structures of the problems. We previously proposed a mutation-based EA that does not use a recombination operator and does not have this problem of the conventional EAs. The mutation-based EA evolves timings at which probabilities for generating phenotypic values (developmental timings) change, and brings different evolution speed to each phenotypic variable, so that it can solve a given problem hierarchically. In this paper we first propose the evolutionary algorithm evolving developmental timing (EDT) by adding a crossover operator to the mutation-based EA and then devise a new test problem that conventional EAs are likely to fail in solving and for which the features of the proposed EA are well utilized. The test problem consists of multiple deceptive problems among which there is hierarchical dependency, and has the feature that the hierarchical dependency is represented by a graph structure. We apply the EDT and the conventional EAs, the PBIL and cGA, for comparison to the new test problem and show the usefulness of the evolution of developmental timing.

关键词： developmental timing deceptive problem graph structure dependency between variables estimation distribution algorithm

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An estimation distribution algorithm with the spearman's rank correlation index 08

An estimation distribution algorithm with the spearman's ran...

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Proceedings of the 10th annual conference on Genetic and evolutionary computation

作者： Arturo Hernández-Aguirre Enrique Villa-Diharce Selma Barba-Moreno Centro de Investigación en Matemáticas Guanajuato Mexico

ISBN: (纸本)9781605581309

This article arguments that rank correlation coefficients are powerful association measures and how can they be adopted by EDAs. A new EDA implements the proposed ideas: the Non-Parametric Real-valued estimation distribution algorithm (NOPREDA). The paper fully describes the rank correlation coefficient, and the procedure to build a non parametric model for the probability distribution of the source data. A benchmark of global optimization problems is solved with NOPREDA.

关键词： rank correlation estimation distribution algorithm

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Solving single row facility layout problem using extended artificial chromosome genetic algorithm

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Journal of Technology 2012年第4期27卷 189-194页

作者： Utamima, Amalia Ou-Yang, Chao Department of Industrial Management National Taiwan University of Science and Technology Taipei City 106 Taiwan Department of Information System Institut Teknologi Speuluh Nopember Surabaya 60111 Indonesia

The layout positioning problem of facilities on a straight line is known as Single Row Facility Layout Problem (SRFLP). The objective of SRFLP, categorized as NP-Complete problem, is to arrange the layout such that the sum of distances between all facilities' pairs can be minimized. Extended Artificial Chromosome Genetic algorithm (eACGA) is a promising algorithm that has been proposed recently. eACGA extends the probabilistic model in estimation of distribution algorithms (EDAs) and then hybridize it with Genetic algorithms (GAs). eACGA is proven to produce an excellent solution for scheduling problem. In this paper, we modify the eACGA to solve SRFLP. Computational results on benchmark problems show the effectiveness of eACGA for solving SRFLP.

关键词： estimation distribution algorithm Genetic algorithm Single row facility layout

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