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A peak-over-threshold search method for global optimization

AUTOMATICA 2018年 89卷 83-91页

作者： Gao, Siyang Shi, Leyuan Zhang, Zhengjun City Univ Hong Kong Dept Syst Engn & Engn Management Tat Chee Ave Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China Univ Wisconsin Dept Ind & Syst Engn 1513 Univ Ave Madison WI 53706 USA Univ Wisconsin Dept Stat 1300 Univ Ave Madison WI 53706 USA

In this paper, we propose a random search method, called peak-over-threshold search (POTS), for solving global optimization problems. An important feature of POTS is that it combines the existing partition based random search framework (e.g., Shi and Olafsson 2000a: Chen et al. 2011) with the peak-over threshold statistical reference (Coles, 2001) in order to achieve high search efficiency. In each iteration, POTS partitions the solution space into several subregions, evaluates the quality of each subregion and moves to promising subregions for more partitioning and sampling. To effectively assess the quality of a subregion, an extreme value type of inference in statistics is used to develop a new promising index which reflects the optimal objective value of a subregion and biases the search to regions that are likely to contain the optimal or near-optimal solutions. Under assumptions on the depth of partitioning and the probability of correct movement, POTS is shown to converge with probability one to the optimal region. The higher efficiency of the proposed method is illustrated by numerical examples. The application of POTS to beam angle selection, an important optimization problem in radiation treatment, is also presented in this paper. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Partition-based random search Global optimization Extreme value analysis meta heuristic algorithm

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A Multi-Depot Home Care Routing Problem with Time Windows and Fuzzy Demands Solving by Particle Swarm Optimization and Genetic algorithm

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IFAC-PapersOnLine 2018年第11期51卷 358-363页

作者： Tohidifard, M. Tavakkoli-Moghaddam, R. Navazi, F. Partovi, M. School of Industrial Engineering College of Engineering University of Tehran Tehran Iran

Providing sanitarian and medical treatments for patients in their location has lots of benefits. Having ordered and on-time medical services cause faster recovery for patients, besides reducing their expenditure. This study presents a new mathematical model for a multi-depot vehicle routing problem (VRP) with time windows for home health care firms. The multi-depot VRP tries to visit some patients and give necessary services to them by several vehicles settled at multiple depots. The endeavor of this problem is raising the satisfaction of patients while minimizing the distance and time of the traveling tour, the number of vehicles, and the transportation cost in a patient's hard time windows. The problem is solved by genetic algorithm (GA) and particle swarm optimization (PSO). The related results are compared with the GAMS outcomes for small-sized problems. © 2018

关键词： Particle swarm optimization (PSO) Genetic algorithms heuristic algorithms Home health care Patient rehabilitation Routing algorithms Vehicle routing Vehicles Medical services Medical treatment meta heuristic algorithm Multi depot vehicle routing problems New mathematical model Number of vehicles Routing problems Transportation cost

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Mobile Robot Path Planning based on Conformal Geometric Algebra and Teaching-Learning Based Optimization

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IFAC-PapersOnLine 2018年第13期51卷 338-343页

作者： Hernandez-Barragan, Jesus University of Guadalajara CUCEI Computer Science Department United States

A crucial task for mobile robots is to move safely through its environment. Therefore, the determination of a safe path planning algorithm is a key problem. In this paper, the authors present a path planning approach based on Conformal Geometric Algebra and Teaching-Learning Based Optimization. The proposed approach solves the path planning for 2D and 3D environments. The simulation results proved the effectiveness of the proposed method. © 2018

关键词： Motion planning Algebra Geometry Mobile robots Optimization Robot programming 3 D environments Conformal Geometric Algebra Geometric Algebra meta heuristic algorithm Robot path planning Safe path planning Teaching learning based optimizations

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Electrochemical Model Parameter Estimation of a Lithium-Ion Battery Using a metaheuristic algorithm: Cascaded Improved Harmony Search

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IFAC-PapersOnLine 2018年第28期51卷 409-413页

作者： Chun, Huiyong Han, Soohee Pohang37673 Korea Republic of

For the safe and accurate operation of lithium-ion battery, the estimation of inner states of battery is necessary. Among many models of battery, electrochemical model, especially P2D model, represents the real dynamics such as transport and diffusion phenomena of charges and lithium-ions. For this reason, P2D model is an appropriate model for estimating the states. In this paper, metaheuristic algorithm is applied to estimate these parameter values, since it is more useful to implement the metaheuristic method than other jacobian-based methods, if the number of the parameters to be estimated is increased. Harmony search is one of the metaheuristic algorithms, and improved harmony search was developed for high convergence rate and fine-tunning. The cascaded version of improved harmony search is proposed as the estimation algorithm in this paper, and it is the first time to using the harmony search for lithium-ion battery parameterization. As a result, the parameters had reasonable values compared to reference values and lower voltage, temperature errors compared to the results of improved harmony search which is employed as a benchmark. © 2018

关键词： Lithium-ion batteries Ions Parameter estimation Appropriate models Convergence rates Electrochemical modeling Estimation algorithm Improved harmony searches meta heuristic algorithm meta heuristic methods Transport and diffusions

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Online Learning algorithm for LSSVM Based Modeling with Time-varying Kernels

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IFAC-PapersOnLine 2018年第18期51卷 626-630页

作者： Kong, Weijian Ding, Jinliang Department of Automation Donghua University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Online learning based Least Squares Support Vector Machine (LSSVM) can address the modeling problems of a time-varying process, which has a few advantages such as low training time and good general. Nevertheless, many of online learning algorithms cannot adapt the kernel parameters for the time-varying characteristic, so the inferred LSSVM models are low-accuracy. An online learning algorithm with time-varying kernels is proposed to improve online training accuracy of LSSVM model. The kernel parameters are optimized along with time-varying process using updating samples data. To achieve reliable performance during online optimization, we propose a controllable metaheuristic algorithm that adopts a contracted particle swarm optimization with an elaborate chaotic operator. The proposed modeling approach is utilized in the energy efficiency prediction of the electrical smelting process, and the experimental results show that the proposed online learning algorithm can both improve the accuracy of LSSVM model and ensure low online training time. © 2018

关键词： Learning algorithms E learning Energy efficiency Learning systems Online systems Particle swarm optimization (PSO) Support vector machines LSSVM meta heuristic algorithm Online learning Online optimization Time varying process

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An adaptive control study for a DC motor using meta-heuristic algorithms

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IFAC-PapersOnLine 2017年第1期50卷 13114-13120页

作者： Rodríguez-Molina, Alejandro Villarreal-Cervantes, Miguel Gabriel Aldape-Pérez, Mario Departamento de Posgrado LGAC de Mecatrónica Instituto Politécnico Nacional CIDETEC CDMX Av. Juan de Dios Bátiz s/n 07700 Mexico

In this work, a comparative study of the use of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. Several adaptive controllers based on the optimizers of Differential Evolution (DE), Particle Swarm Optimization (PSO), Bat algorithm (BAT), Firefly algorithm (FFA) and Wolf Search algorithm (WSA) are proposed in order to on-line tune the parameters of the DC motor. These parameters are used in calculating the control signal. Simulations show the efficacy of each control strategy. Given the results, the controller based on PSO is one of the most promising alternatives for this approach. © 2017

关键词： Electric machine control Adaptive control systems Controllers DC motors heuristic algorithms heuristic methods Parameter estimation Particle swarm optimization (PSO) Adaptive Control Differential Evolution heuristics meta heuristic algorithm meta heuristic techniques Optimization problems Output regulation Parameters uncertainties

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A Hybridized Model for Image Encryption through Genetic algorithm and DNA Sequence 6

A Hybridized Model for Image Encryption through Genetic Algo...

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6th International Conference on Smart Computing and Communications (ICSCC)

作者： Pujari, Saswat K. Bhattacharjee, Gargi Bhoi, Soumyakanta Tata Consultancy Serv Bombay Maharashtra India Veer Surendra Sai Univ Technol Dept Informat Technol Burla Odisha India Wipro Technol Hyderabad Andhra Pradesh India

With the advent of multimedia technology and rapid data transmission ability of networks, images have become a vital source of extracting information. So its security becomes a critical issue. Images are encrypted to another form in order to preserve their data. In this paper, we have put forward a hybridized model for encrypting images through a combination of Genetic algorithm and DNA Sequence. DNA sequence is basically chosen as it offers greater storage and higher computing capabilities. The encryption method consists of two phases-Transposition or Scrambling phase and Substitution phase. In the first phase, pixel locations are altered using GA to reduce the correlation among adjacent pixels. In substitution phase, the pixels are replaced by using XOR operation between the pixel values converted into binary strings and DNA substrings derived from a random DNA string. DNA substrings are used as keys for image encryption. The experimental outcome validates that the algorithm is simple, fast enough and feasible. Performance analysis asserts the robustness of the algorithm against all kinds of attacks and thereby maintaining higher security. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： Image Encryption Genetic algorithm DNA Sequence meta heuristic algorithm Image Cryptosystem Image Security

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A Hybridized Model for Image Encryption through Genetic algorithm and DNA Sequence

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Procedia Computer Science 2018年 125卷 165-171页

作者： Saswat K Pujari Gargi Bhattacharjee Soumyakanta Bhoi Tata Consultancy Services Mumbai India Department of Information Technology Veer Surendra Sai University of Technology Burla Odisha India Wipro Technlogies Hyderabad Andhra Pradesh India

With the advent of multimedia technology and rapid data transmission ability of networks, images have become a vital source of extracting information. So its security becomes a critical issue. Images are encrypted to another form in order to preserve their data. In this paper, we have put forward a hybridized model for encrypting images through a combination of Genetic algorithm and DNA Sequence. DNA sequence is basically chosen as it offers greater storage and higher computing capabilities. The encryption method consists of two phases- Transposition or Scrambling phase and Substitution phase. In the first phase, pixel locations are altered using GA to reduce the correlation among adjacent pixels. In substitution phase, the pixels are replaced by using XOR operation between the pixel values converted into binary strings and DNA substrings derived from a random DNA string. DNA substrings are used as keys for image encryption. The experimental outcome validates that the algorithm is simple, fast enough and feasible. Performance analysis asserts the robustness of the algorithm against all kinds of attacks and thereby maintaining higher security.

关键词： Image Encryption Genetic algorithm DNA Sequence meta heuristic algorithm Image Cryptosystem Image Security

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Improved Dynamic Virtual Bats algorithm for Global Numerical Optimization

Improved Dynamic Virtual Bats Algorithm for Global Numerical...

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World Congress on Engineering and Computer Science, WCES 2017

作者： Topal, Ali Osman Yildiz, Yunus Emre Ozkul, Mukremin Epoka Univ Dept Comp Engn Tirana Albania Metrapolitan Univ Dept Comp Engn Tirana Albania

ISBN: (纸本)9789881404756

Dynamic Virtual Bats algorithm (DVBA) is a relatively new nature inspired optimization algorithm. DVBA, like Bat algorithm (BA), is fundamentally inspired by bat's hunting strategies, but it is conceptually very different from BA. In DVBA, a role based search is developed to avoid deficiencies of BA. Although the new technique outperforms BA significantly, there is still an insufficiency in DVBA regarding its exploration, when it comes to high dimensional complex optimization problems. To increase the performance of DVBA, this paper presents a novel, improved dynamic virtual bats algorithm (IDVBA) based on probabilistic selection. The performance of the proposed IDVBA was compared with DVBA and four state-of-the-art BA variants. The algorithms were tested on 30 optimization problems from CEC 2014. The experimental results demonstrated that the new search mechanism improved the performance of DVBA in terms of accuracy and robustness.

关键词： dynamic virtual bats algorithm meta heuristic algorithm global numerical optimization

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LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning

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IFAC-PapersOnLine 2016年第5期49卷 353-358页

作者： Derouiche, Mohamed Lotfi Bouallègue, Soufiene Haggège, Joseph Sandou, Guillaume National Engineering School of Tunis BP 37 Le Belvédère Tunis1002 Tunisia L2S CentraleSupélec CNRS Université Paris Sud Université Paris-Saclay 3 rue Joliot Curie Gif-sur-Yvette Cedex91192 France

In this paper, a new Model Predictive Controller (MPC) parameters tuning strategy is proposed using a Lab VIEW-based perturbed Particle Swarm Optimization (pPSO) approach. This original LabVIEW implementation of this metaheuristic algorithm is firstly validated on some test functions in order to show its efficiency and validity. The optimization results are compared with the standard PSO approach. The parameters tuning problem, i.e. the weighting factors on the output error and input increments of the MPC algorithm, is then formulated and systematically solved, using the proposed LabVIEW pPSO algorithm. The case of a Magnetic Levitation (MAGLEV) system is investigated to illustrate the robustness and superiority of the proposed pPSO-based tuning MPC approach. All obtained simulation results, as well as the statistical analysis tests for the formulated control problem with and without constraints, are discussed and compared with the Genetic algorithm Optimization (GAO)-based technique in order to improve the effectiveness of the proposed pPSO-based MPC tuning methodology. © 2016

关键词： Particle swarm optimization (PSO) Genetic algorithms Magnetic levitation Magnetic levitation vehicles Model predictive control Parameter estimation Predictive control systems Genetic algorithm optimizations LabViEW Maglev Systems Magnetic levitation (maglev) meta heuristic algorithm Model predictive controllers Parameters tuning Weighting factors

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