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hybrid evolutionary algorithms in a SVR traffic flow forecasting model

APPLIED MATHEMATICS AND COMPUTATION 2011年第15期217卷 6733-6747页

作者： Hong, Wei-Chiang Dong, Yucheng Zheng, Feifeng Wei, Shih Yung Oriental Inst Technol Dept Informat Management Taipei 220 Taiwan Xi An Jiao Tong Univ Sch Management Dept Management & Org Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Management Dept Management Sci Xian 710049 Peoples R China Natl Yunlin Univ Sci & Technol Dept Finance Touliu 640 Yunlin Taiwan Natl Yunlin Univ Sci & Technol Grad Inst Finance Touliu 640 Yunlin Taiwan

Accurate urban traffic flow forecasting is critical to intelligent transportation system developments and implementations, thus, it has been one of the most important issues in the research on road traffic congestion. Due to complex nonlinear data pattern of the urban traffic flow, there are many kinds of traffic flow forecasting techniques in literature, thus, it is difficult to make a general conclusion which forecasting technique is superior to others. Recently, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a SVR traffic flow forecasting model which employs the hybrid genetic algorithm-simulated annealing algorithm (GA-SA) to determine its suitable parameter combination. Additionally, a numerical example of traffic flow data from northern Taiwan is used to elucidate the forecasting performance of the proposed SVRGA-SA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), Holt-Winters (HW) and seasonal Holt-Winters (SHW) models. Therefore, the SVRGA-SA model is a promising alternative for forecasting traffic flow. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Traffic flow forecasting Support vector regression hybrid genetic algorithm-simulated annealing algorithm (GA-SA) hybrid evolutionary algorithms SARIMA Back-propagation neural network BPNN Holt-Winters (HW) Seasonal Holt-Winters (SHW)

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hybrid evolutionary algorithms for constraint satisfaction problems: Memetic overkill?

Hybrid evolutionary algorithms for constraint satisfaction p...

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IEEE Congress on evolutionary Computation

作者： Craenen, BGW Eiben, AE Napier Univ Edinburgh EH10 5DT Midlothian Scotland

ISBN: (纸本)0780393635

We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contribution of the evolutionary component to their performance by comparing the hybrid EAs with their "de-evolutionarised" variants. The experiments show that "de-evolutionarising" can increase performance, in some cases doubling it. Considering that the problem domain and the algorithms are arbitrarily selected from the "memetic niche", it seems likely that the same effect occurs for other problems and algorithms. Therefore, our conclusion is that after designing and building a memetic algorithm, one should perform a verification by comparing this algorithm with its "de-evolutionarised" variant.

关键词： computability computational complexity constraint theory evolutionary computation knowledge verification constraint satisfaction problems de-evolutionarisation hybrid evolutionary algorithms memetic algorithms

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hybrid evolutionary algorithms Design Based on Their Advantages

Hybrid Evolutionary Algorithms Design Based on Their Advanta...

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5th International Symposium on Intelligence Computation and Applications

作者： Lin, Guangming Liu, Sundong Tang, Fei Wang, Huijie Shenzhen Inst Informat Technol Shenzhen Peoples R China Shenzhen Uni Sch Informat Technol & Elect Engn Shenzhen Peoples R China

ISBN: (纸本)9783642164927

The search direction and the search step size are two important factors which affect the performance of algorithms. In this paper, we combine Particle Swarm Optimization (PSO) with EP to form two new algorithms namely PSOEP and SAVPSO. The basic idea is to introduce the search direction to the mutation operator of EP and use lognormal self-adaptive strategy to control the velocity of PSO to guide the individual at a faster convergence rate. All of these algorithms are compared to each other with respect to the similarities and differences of their basic components, as well as their performances on seven benchmark problems. Our experimental results show that PSOEP performs much better than all other version of EPs, and SAVPSO performs much better than PSO for the benchmark functions.

关键词： hybrid evolutionary algorithms Search Direction Search Step Size Control

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Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms

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APPLIED ENERGY 2013年 101卷 655-666页

作者： Gitizadeh, Mohsen Vahed, Ali Azizi Aghaei, Jamshid Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

The main goal of this paper is to present a Multistage Distribution network Expansion Planning (MDEP) problem in the presence of Distributed Generations (DGs) in a multi-objective optimization framework. The proposed model simultaneously optimizes two objectives: minimization of investment and operation costs and maximization of reliability index. The proposed optimization model is solved subject to AC power flow constraints to obtain the optimal configuration of feeders (adding and removing lines) including the optimal size of branch conductor, replacement of conductor for reserve feeders, and generated power of DGs. To include reliability concerns in the proposed MDEP problem, an analytical approach on the basis of graph theory is implemented to evaluate the Energy-Not-Supplied (ENS) index as an extra objective function. Also, in this paper, in order to identify Pareto optimal solutions of the multi-objective MDEP problem, a hybrid Particle Swarm Optimization (PSO) and Shuffled Frog Leaping (SFL) algorithm is implemented. A synthetic distribution test system is considered for the MDEP problem in a 4-year planning horizon. The results of the hybrid PSO and SFL algorithm are compared with those of the classical PSO and SFL methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multi-objective MDEP Distributed generation Energy-not-sup plied hybrid evolutionary algorithms

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Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 112卷 616-633页

作者： Gen, Mitsuo Zhang, Wenqiang Lin, Lin Yun, YoungSu Fuzzy Log Syst Inst Dept Res Iizuka Fukuoka Japan Tokyo Univ Sci Res Inst Sci & Technol Tokyo 162 Japan Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Henan Peoples R China Minist Educ Key Lab Grain Informat Proc & Control Zhengzhou Henan Peoples R China Dalian Univ Technol Sch Software Engn Dalian Peoples R China Chosun Univ Div Business Adm Gwangju South Korea

In real manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues with multiple objectives. However it is very difficult for solving the intractable COP problems by the traditional approaches because of NP-hard problems. For developing effective and efficient algorithms that are in a sense "good," i.e., whose computational time is small as within 3 min, we have to consider three issues: quality of solution, computational time and effectiveness of the nondominated solutions for multiobjective optimization problem (MOP). In this paper, we focus on recent hybrid evolutionary algorithms (HEA) to solve a variety of single or multiobjective scheduling problems in manufacturing systems to get a best solution with a smaller computational time. Firstly we summarize multiobjective hybrid genetic algorithm (Mo-HGA) and hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MoEA) and then propose HSS-MoEA combining with differential evolution (HSS-MoEA-DE). We also demonstrate those hybrid evolutionary algorithms to bicriteria automatic guided vehicle (B-AGV) dispatching problem, robot-based assembly line balancing problem (R-ALB), bicriteria flowshop scheduling problem (B-FSP), multiobjective scheduling problem in thin-film transistor-liquid crystal display (TFT-LCD) module assembly and bicriteria process planning and scheduling (B-PPS) problem. Also we demonstrate their effectiveness of the proposed hybrid evolutionary algorithms by several empirical examples. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Combinatorial optimization problem Multiobjective optimization problem hybrid evolutionary algorithms Genetic algorithm AGV dispatching Assembly line balancing Flowshop scheduling model TFT-LCD module assembly model Process planning and scheduling model

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A survey: hybrid evolutionary algorithms for cluster analysis

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ARTIFICIAL INTELLIGENCE REVIEW 2011年第3期36卷 179-204页

作者： Abul Hasan, Mohamed Jafar Ramakrishnan, Sivakumar AVVM Sri Pushpam Coll Autonomous Dept Comp Sci Poondi Tamil Nadu India

Clustering is a popular data analysis and data mining technique. It is the unsupervised classification of patterns into groups. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as slowness of the convergence, sensitive to initial value and preset classed in large scale data set etc. and they still require much investigation to improve performance and efficiency. Over the last decade, clustering with ant-based and swarm-based algorithms are emerging as an alternative to more traditional clustering techniques. Many complex optimization problems still exist, and it is often very difficult to obtain the desired result with one of these algorithms alone. Thus, robust and flexible techniques of optimization are needed to generate good results for clustering data. Some algorithms that imitate certain natural principles, known as evolutionary algorithms have been used in a wide variety of real-world applications. Recently, much research has been proposed using hybrid evolutionary algorithms to solve the clustering problem. This paper provides a survey of hybrid evolutionary algorithms for cluster analysis.

关键词： Data mining Cluster analysis Swarm intelligence hybrid evolutionary algorithms

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On The Role Of Execution Order In hybrid evolutionary algorithms

On The Role Of Execution Order In Hybrid Evolutionary Algori...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： LaTorre, Antonio Molina, Daniel Univ Politecn Madrid Ctr Computat Simulat CCS Madrid Spain Univ Granada Andalusian Inst Data Sci DASCI Granada Spain Univ Granada Computat Intelligence Dept Granada Spain

ISBN: (纸本)9781728169293

Many real-world problems can be formulated as the optimization of a continuous function. Furthermore, these problems are becoming increasingly more complex every year, reaching, or even exceeding, the thousand of variables. evolutionary algorithms have been traditionally successful at solving this kind of problems, due to their good balance in terms of solution quality and computation time. However, the aforementioned growth in the size of the problems requires of novel approaches to deal with the increased complexity of larger solutions spaces. hybrid evolutionary algorithms are a powerful alternative in these scenarios as they are able to combine the strengths of multiple search methods to solve more complex problems. These hybrid approaches normally do not pay attention to the execution order of their components, being the most frequent strategy to always run them in a predefined sequence. In this contribution we study the role of execution order in hybrid evolutionary algorithms within the context of the multiple offspring sampling framework, one of the best algorithms in large-scale global optimization. As shown in the experimentation, a proper execution order policy can boost the performance of MOS to improve the results of other state-of-the-art algorithms.

关键词： Large-scale global optimization LSGO hybrid evolutionary algorithms Memetic algorithm Execution order

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Kohonen neural networks and hybrid evolutionary algorithms as tools for understanding and forecasting of plankton dynamics in response to food web manipulation in Lake Wolderwijd (The Netherlands)

Kohonen neural networks and hybrid evolutionary algorithms a...

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第三世界生态高峰会

作者： Anita Talib Friedrich Recknagel Diederik van der Molen Universiti of Science Malaysia Minden 11800 Penang Malaysia School of Earth and Environmental Sciences University of Adelaide 5005 Australia Universiti of Science Malaysia Minden 11800 Penang Malaysia Instiute of Inland Water Management and Waste Waste Water Treatment.8200 AA Lelystad The Netherlands

Kohonen artificial neural networks(KANN)and hybrid evolutionary algorithms(HEA)were applied for time-series modelling of the shallow eutrophic Lake *** the lake was subject to a variety of eutrophication management approaches over the past 3 decades,the aim of the time-series modelling was to assess the efficiency of implemented management *** KANN were applied for ordination and clustering lake conditions regarding pre- and post management effects on the phyto- and zooplankton dynamics, HEA were trained and tested to forecast potential management effects in the *** of the study have demonstrated that:(1) KANN can elucidate short-term and long-term causal relationships of complex ecological dynamics,and(2)HEA can forecast 5-days-ahead chlorophyll-a,Bosmina,Daphnia abundance as well as clear-water *** have demonstrated that KANN and HEA can successfully be used to explore information in long-term time-series data of lake ecosystems for the assessment and forecasting of lake ecosystem health.

关键词： Kohonen ANN hybrid evolutionary algorithms food-web manipulation phytoplankton zooplankton eutrophication control

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PESA: Prioritized experience replay for parallel hybrid evolutionary and swarm algorithms-Application to nuclear fuel

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NUCLEAR ENGINEERING AND TECHNOLOGY 2022年第10期54卷 3864-3877页

作者： Radaideh, Majdi I. Shirvan, Koroush MIT Dept Nucl Sci & Engn Cambridge MA 02139 USA

We propose a new approach called PESA (Prioritized replay evolutionary and Swarm algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hy-bridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm;unleashing the power of parallel computing for expensive optimization.(c) 2022 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： hybrid evolutionary algorithms Reinforcement learning Parallel optimization Priortized experience replay Nuclear fuel optimization

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Portfolio construction using argumentation and hybrid evolutionary forecasting algorithms

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International Journal of hybrid Intelligent Systems 2009年第4期6卷 231-243页

作者： Spanoudakis, Nikolaos Pendaraki, Konstantina Beligiannis, Grigorios Applied Mathematics and Computers Laboratory Department of Sciences Technical University of Crete University Campus 73100 Kounoupidiana Greece Department of Business Administration of Food and Agricultural Enterprises University of Ioannina G. Seferi 2 30100 Agrinio Greece

In the current contribution, an application for constructing mutual fund portfolios is presented. This approach comprises several Intelligent Methods, namely an argumentation based decision making framework and a hybrid evolutionary forecasting algorithm which combines Genetic algorithms (GA), MultiModel Partitioning (MMP) theory and Extended Kalman Filters (EKF). Specifically, the argumentation framework is employed in order to develop mutual funds performance models and select a small set of mutual funds, which will compose the final portfolio. On the other hand, the hybrid evolutionary forecasting algorithm is applied in order to forecast the market status (inflating or deflating) for the next investment period. The knowledge engineering approach and application development steps are also presented and discussed.

关键词： Portfolio construction mutual funds intelligent methods argumentation forecasting hybrid evolutionary algorithms

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