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Fast reliability evaluation method for composite power system based on the improved EDA and double cross linked list

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IET GENERATION TRANSMISSION & distribution 2017年第15期11卷 3835-3842页

作者： Liu, Wenxia Cheng, Rui Xu, Yahui Liu, Zongqi North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China

To improve the computational efficiency of Monte Carlo simulation in composite power systems reliability evaluation, this study presents a method based on the improved estimation of distribution algorithm (EDA) and double cross linked list. Compared to traditional techniques, this method is comprehensively improved in the stage of both sampling and state evaluation. In the sampling stage, the population-based incremental learning algorithm is presented, where the probability vector is updated based on the distribution characteristics of excellent samples in population of previous generations. Meanwhile, setting a limit to the probabilities of elements in normal state and mutation strategy are introduced, which improves the excellent characteristics of the population. In the state evaluation stage, the state search and match process is speed up by utilising the intelligent storage technology based on the double across linked list. It avoids calling the optimal power flow for the same state repeatedly. Finally, the proposed method is tested in IEEE RTS 79. As the result shows, compared with other methods ever used in reliability evaluation, this method is not only more efficient in computation but also more accurate. Thus, the proposed method is proved to be reliable and effective.

关键词： power system reliability Monte Carlo methods vectors fast reliability evaluation method EDA double cross linked list Monte Carlo simulation composite power systems reliability evaluation estimation of distribution algorithm population-based incremental learning algorithm probability vector mutation strategy state evaluation stage state search match process intelligent storage technology optimal power flow

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Online Ecological Energy Management for Plug-In HEVs Using Optimal SOC Prediction and Stochastic Optimization

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第9期25卷 10885-10895页

作者： Lim, Hansang Kwangwoon Univ Dept Elect Convergence Engn Seoul 01897 South Korea

Optimizing power demand and distribution in real time is the primary challenge in the energy management of plug-in hybrid electric vehicles. To address this challenge, this study proposes an online ecological energy management strategy using the optimal state of charge (SOC) prediction and stochastic optimization. Future energy-consumption features at each location are computed from the traffic and route information of the entire remaining trip and the optimal SOC value at the next location is predicted from the computed features using a recurrent neural network. By considering the predicted optimal SOC and average speed as references, the speed and SOC values at the next location are optimized using a modified estimation of distribution algorithm (EDA). The process of generating initial and new populations in EDA is modified for fast and reliable convergence. The optimal SOC prediction considering the energy consumption for the entire remaining trip from the current to final locations ensures long-term optimality. Stochastic aspects of the modified EDA can compensate for potential neural-network errors. The prediction and optimization of driving only up to the next location enables to adapt to the changes in traffic conditions. Moreover, the energy efficiency is further improved by optimizing power demand based on the characteristics of the drivetrain, as well as traffic and route conditions. Because the traffic and route information are provided on a location basis, management is performed in a distance domain. The proposed management strategy is evaluated in diverse driving scenarios, and it exhibits good performance, comparable to that of the offline optimization.

关键词： State of charge Optimization Batteries Real-time systems Biological system modeling Motors Power distribution estimation of distribution algorithm feed-forward neural network long-term optimality online ecological energy management optimal speed and state-of-charge recurrent neural network

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Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models

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NEURAL NETWORKS 2009年第5-6期22卷 623-632页

作者： Schliebs, Stefan Defoin-Platel, Michael Worner, Sue Kasabov, Nikola Auckland Univ Technol KEDRI Auckland New Zealand Lincoln Univ Ctr Bioprotect Canterbury New Zealand

This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are Studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an Optimal Solution (or a close to it), in a better accuracy, and in a more informative set of features selected. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Evolving spiking neural network Quantum-inspired evolutionary algorithm Multiple probabilistic model estimation of distribution algorithm

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Continuous probabilistic model building genetic network programming using reinforcement learning

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APPLIED SOFT COMPUTING 2015年 27卷 457-467页

作者： Li, Xianneng Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan Waseda Univ Informat Prod & Syst Res Ctr Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

Recently, a novel probabilistic model-building evolutionary algorithm (so called estimation of distribution algorithm, or EDA), named probabilistic model building genetic network programming (PMBGNP), has been proposed. PMBGNP uses graph structures for its individual representation, which shows higher expression ability than the classical EDAs. Hence, it extends EDAs to solve a range of problems, suchas data mining and agent control. This paper is dedicated to propose a continuous version of PMBGNP for continuous optimization in agent control problems. Different from the other continuous EDAs, the proposed algorithm evolves the continuous variables by reinforcement learning (RL). We compare the performance with several state-of-the-art algorithms on a real mobile robot control problem. The results show that the proposed algorithm outperforms the others with statistically significant differences. (C) 2014 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithm Probabilistic model building genetic network programming Continuous optimization Reinforcement learning

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An Individual and Model-Based Offspring Generation Strategy for Evolutionary Multiobjective Optimization

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IEEE ACCESS 2019年 7卷 34675-34686页

作者： Du, Guanjun Tong, Guoxiang Xiong, Naixue Univ Shanghai Sci & Technol Shanghai Key Lab Modern Opt Syst Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA

According to the Karush-Kuhn-Tucker condition, the Pareto set (PS) of a continuous m-objective optimization problem is a continuous (m - 1)-D piecewise manifold. Based on this regularity property, the ratio of the sum of the first (m - 1) largest eigenvalue of the population's covariance matrix to the sum of the whole eigenvalue can be employed to illustrate the degree of convergence of the population. This paper proposes a new algorithm, named DE/RM-MEDA, which hybridizes differential evolution (DE) and estimation of distribution algorithm (EDA) for multiobjective optimization problems (MOPs) with the complicated PS. In the proposed algorithm, EDA extracts the population distribution information to sample new trial solutions by establishing a probability model, while DE uses the individual information to create others new individuals through the mutation and crossover operators. At each generation, the number of new solutions generated by the two operators is adjusted by the above-defined ratio. The proposed algorithm is validated on nine tec09 problems. The sensitivity and the scalability have also been experimentally investigated in this paper. The comparison results between DE/RM-MEDA and the other two state-of-the-art evolutionary algorithms, namely NSGA-II-DE and RM-MEDA, show that the proposed algorithm is highly competitive algorithms for solving MOPs with complicated PSs in terms of convergence and diversity metrics.

关键词： Differential evolution estimation of distribution algorithm hybrid algorithm reproduction operators

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Collaborative Optimization Design for Centralized Networked Control System

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IEEE ACCESS 2021年 9卷 19479-19487页

作者： Yan, Xiang Li, Jun Mei, Bizhou Zhejiang Wanli Univ Sch Informat & Intelligence Engn Ningbo 315000 Peoples R China Zhejiang Yi Duan Machinery Co Ningbo 315000 Peoples R China

This paper proposes a collaborative optimization design for a kind of centralized networked control system based on jitter. After the analysis of the network delay and jitter on the performance of the Train Networked Control System (TNCS) based on the MVB (Multifunction Vehicle Bus) network, the proposed strategy modifies the media allocating model of MVB directly related to the performance of the control system. Under the premise of ensuring the stability of the control system, and taking into account the impact of transmission jitter on the dynamic performance of the closed-loop control, this collaborative design method can minimize the network resource occupancy rate of the subsystem. Thus, it can overcome schedule failure in the traditional algorithm that excessively occupies network resources in order to reduce jitter. Finally, the authors present an algorithm based on EDA to find the optimal solution of the proposed strategy and illustrate the effectiveness of the strategy through numerical simulation and experimental tests.

关键词： Delays Control systems Collaboration Stability analysis Jitter Networked control systems Real-time systems Train networked control system jitter estimation of distribution algorithm

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Not all PBILs are the same: Unveiling the different learning mechanisms of PBIL variants

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APPLIED SOFT COMPUTING 2017年 53卷 88-96页

作者： Zangari, M. Santana, R. Mendiburu, A. Pozo, A. T. R. Univ Fed Parana Dlnf CP 19081 BR-19031970 Curitiba Parana Brazil Univ Basque Country Intelligent Syst Grp San Sebastian Spain

Model-based optimization using probabilistic modeling of the search space is one of the areas where research on evolutionary algorithms (EAs) has considerably advanced in recent years. The population based incremental algorithm (PBIL) is one of the first algorithms of its kind and it has been extensively applied to many optimization problems. In this paper we show that the different applications of PBIL reported in the literature correspond, in fact, to two essentially different algorithms, which are defined by the way the learning step is implemented. We analytically and empirically study the impact of the learning method on the search behavior of the algorithm. As a result of our research, we show examples in which the choice of a PBIL variant can produce qualitatively different outputs of the search process. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Probabilistic modeling PBIL estimation of distribution algorithm

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Multiple Power Line Outage Detection in Smart Grids: Probabilistic Bayesian Approach

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IEEE ACCESS 2018年 6卷 10650-10661页

作者： Ahmed, Ashfaq Awais, Muhammad Naeem, Muhammad Iqbal, Muhammad Ejaz, Waleed Anpalagan, Alagan Kim, Hongseok COMSATS Inst Informat Technol Islamabad 47040 Pakistan Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada Sogang Univ Dept Elect Engn Seoul 04107 South Korea

Efficient power line outage identification is an important step which ensures reliable and smooth operation of smart grids. The problem of multiple line outage detection (MLOD) is formulated as a combinatorial optimization problem and known to be NP-hard. Such a problem is optimally solvable with the help of an exhaustive evaluation of all possible combinations of lines in outage. However, the size of search space is exponential with the number of power lines in the grid, which makes exhaustive search infeasible for practical sized smart grids. A number of published works on MLOD are limited to identify a small, constant number of lines outages, usually known to the algorithm in advanced. This paper applies the Bayesian approach to solve the MLOD problem in linear time. In particular, this paper proposes a low complexity estimation of outage detection algorithm, based on the classical estimation of distribution algorithm. Thanks to an efficient thresholding routine, the proposed solution avoids the premature convergence and is able to identify any arbitrary number (combination) of line outages. The proposed solution is validated against the IEEE-14 and 57 bus systems with several random line outage combinations. Two performance metrics, namely, success generation ratio and percentage improvement have been introduced in this paper, which quantify the accuracy as well as convergence speed of proposed solution. The comparison results demonstrate that the proposed solution is computationally efficient and outperforms a number of classical meta-heuristics.

关键词： Line outage identification smart grids estimation of distribution algorithm power networks

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C-Multi: A competent multi-swarm approach for many-objective problems

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NEUROCOMPUTING 2016年 180卷 68-78页

作者： Castro, Olacir R., Jr. Santana, Roberto Pozo, Aurora Fed Univ Parana UFPR Dept Comp Sci PO 19081 BR-81531970 Curitiba Parana Brazil Univ Basque Country UPV EHU Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence Paseo Manuel de Lardizabal 1 San Sebastian 20080 Guipuzcoa Spain

One of the major research topics in the evolutionary multi-objective community is handling a large number of objectives also known as many-objective optimization problems (MaOPs). Most existing methodologies have demonstrated success for problems with two and three objectives but face significant challenges in many-objective optimization. To tackle these challenges, a hybrid multi-swarm algorithm called C-Multi was proposed in a previous work. The project of C-Multi is based on two phases;the first uses a unique particle swarm optimization (PSO) algorithm to discover different regions of the Pareto front. The second phase uses multiple swarms to specialize on a dedicate part. On each sub swarm, an estimation of distribution algorithm (EDA) is used to focus on convergence to its allocated region. In this study, the influence of two critical components of C-Multi, the archiving method and the number of swarms, is investigated by empirical analysis. As a result of this investigation, an improved variant of C-Multi is obtained, and its performance is compared to I-Multi, a multi-swarm algorithm that has a similar approach but does not use EDAs. Empirical results fully demonstrate the superiority of our proposed method on almost all considered test instances. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimization Many-objective estimation of distribution algorithm Competent algorithm

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DE/EDA: A new evolutionary algorithm for global optimization

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INFORMATION SCIENCES 2005年第3-4期169卷 249-262页

作者： Sun, JY Zhang, QF Tsang, EPK Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

Differential evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. estimation of distribution algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a combination of DE and EDA (DE/EDA) for the global continuous optimization problem. DE/EDA combines global information extracted by EDA with differential information obtained by DE to create promising solutions. DE/EDA has been compared with the best version of the DE algorithm and an EDA on several commonly utilized test problems. Experimental results demonstrate that DE/EDA outperforms the DE algorithm and the EDA. The effect of the parameters of DE/EDA to its performance is investigated experimentally. (C) 2004 Elsevier Inc. All rights reserved.

关键词： differential evolution estimation of distribution algorithm global continuous optimization problem

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