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Identification of Wiener Model with Discontinuous Nonlinearities using differential evolution

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2013年第3期11卷 511-518页

作者： Zhang, Dong-Li Tang, Ying-Gan Ma, Ju-Hai Guan, Xin-Ping Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

This paper deals with the identification of Wiener models with discontinuous nonlinearities. The identification of the Wiener model is formulated as an optimization problem. differential evolution algorithm, a powerful and robust evolutionary algorithm, is used to search the optimal parameter of the Wiener model such that the error between the output of true model and that of the identified model is minimized. The proposed method can identify the parameters of linear dynamic subsystems and static nonlinear function of the Wiener model simultaneously, and overcome the difficulty of unavailability of the intermediated signal. Two application examples verify that the proposed method can accurately estimate the parameters of the Wiener model even in a low SNR environment.

关键词： differential evolution algorithm discontinuous nonlinearities identification wiener model

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Performance evaluation of proposed differential evolution and Particle Swarm Optimization algorithms for scheduling m-machine flow shops with lot streaming

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第1期24卷 175-191页

作者： Chakaravarthy, G. Vijay Marimuthu, S. Sait, A. Naveen Fathima Michael Coll Engn & Technol Dept Mech Engn Madurai 625020 Tamil Nadu India Latha Mathavan Engn Coll Dept Mech Engn Madurai 625301 Tamil Nadu India Chendhuran Coll Engn & Technol Dept Mech Engn Pudukkottai 622507 India

We consider n-job, m-machine lot streaming problem in a flow shop with equal size sub lots where the objective is to minimize the makespan and total flow time. Lot streaming (Lot sizing) is a technique that splits a production lot consisting of identical items into sub lots to improve the performance of a multi stage production system by over lapping the sub lots on successive machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. To solve this problem, we propose a differential evolution algorithm (DEA) and Particle Swarm Optimization (PSO) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set up time. In this research, we propose the DEA and PSO algorithms for discrete lot streaming with equal sub lots. The proposed methods are tested and the performances were evaluated. The computational results show that the proposed algorithms are very competitive for the lot streaming flow shop scheduling problem.

关键词： Flow shop Lot streaming Scheduling differential evolution algorithm Particle swarm optimization algorithm

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Multi-robot mapping based on the adaptive differential evolution

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High Technology Letters 2013年第1期19卷 7-11页

作者：刘利枚 CaiZixing School of Information Science and EngineeringCentral South University School of Computer and Electronic EngineeringHunan University of Commerce

Map building by multi-robot is very important to accomplish autonomous navigation,and one of the basic problems and research hotspots is how to merge the maps into a single one in the field of multi-robot map building.A novel approach is put forward based on adaptive differential evolution to map building for the multi-robot *** multi-robot mapping-building system adopts the methods of decentralized exploration and concentrated *** adaptive differential evolution algorithm is used to search in the space of possible transformation,and the iterative search is performed with the goal of maximizing overlapping *** map is translated and rotated so that the two maps can be overlapped and merged into a single global one *** approach for map building can be realized without any knowledge of their relative *** results show that the approach is effective and feasibile.

关键词： differential evolution algorithm cooperative simultaneous localization and mapping map building multi-robot grid maps adaptive

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Null Control in Linear Antenna Arrays with Ensemble differential evolution

Null Control in Linear Antenna Arrays with Ensemble Differen...

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IEEE Symposium on differential evolution (SDE)

作者： Secmen, Mustafa Tasgetiren, M. Fatih Karabulut, Korhan Yasar Univ Dept Elect & Elect Engn Izmir Turkey Yasar Univ Dept Ind Engn Izmir Turkey Yasar Univ Dept Software Engn Izmir Turkey

ISBN: (纸本)9781467358736

This paper describes a synthesis method for null insertion in linear antenna array geometries by using newly proposed ensemble differential evolution (DE) algorithm. The given ensemble DE algorithm uses the advantages of several types of DE algorithms, and fuses them within a single algorithm. In the application, the algorithm searches for the minimization of the difference between the produced radiation pattern of the antenna array and desired radiation pattern, which contains null(s) at some specific aspect angle(s). Simulation results are illustrated for a Chebyshev radiation pattern and the effectiveness of the algorithm is validated. Besides, the results of ensemble DE algorithm are compared with bees algorithm, and the superiority of the proposed algorithm to bees algorithm is demonstrated.

关键词： Antenna array array synthesis differential evolution algorithm ensemble null control

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Targeted Pressure Management During CO 2 Sequestration: Optimization of Well Placement and Brine Extraction

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Energy Procedia 2014年 63卷 5325-5332页

作者： Abdullah Cihan Jens Birkholzer Marco Bianchi Earth Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA

Large-scale pressure increases resulting from carbon dioxide (CO 2 ) injection in the subsurface can potentially impact caprock integrity, induce reactivation of critically stressed faults, and drive CO 2 or brine through conductive features into shallow groundwater. Pressure management involving the extraction of native fluids from storage formations can be used to minimize pressure increases while maximizing CO 2 storage. However, brine extraction requires pumping, transportation, possibly treatment, and disposal of substantial volumes of extracted brackish or saline water, all of which can be technically challenging and expensive. This paper describes a constrained differential evolution (CDE) algorithm for optimal well placement and injection/extraction control with the goal of minimizing brine extraction while achieving predefined pressure contraints. The CDE methodology was tested for a simple optimization problem whose solution can be partially obtained with a gradient-based optimization methodology. The CDE successfully estimated the true global optimum for both extraction well location and extraction rate, needed for the test problem. A more complex example application of the developed strategy was also presented for a hypothetical CO 2 storage scenario in a heterogeneous reservoir consisting of a critically stressed fault nearby an injection zone. Through the CDE optimization algorithm coupled to a numerical vertically-averaged reservoir model, we successfully estimated optimal rates and locations for CO 2 injection and brine extraction wells while simultaneously satisfying multiple pressure buildup constraints to avoid fault activation and caprock fracturing. The study shows that the CDE methodology is a very promising tool to solve also other optimization problems related to GCS, such as reducing ‘Area of Review’, monitoring design, reducing risk of leakage and increasing storage capacity and trapping.

关键词： carbon sequestration pressure management brine extraction optimization differential evolution algorithm

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Robust Protective Relay Setting and Coordination Using Modified differential evolution Considering Different Network Topologies

Robust Protective Relay Setting and Coordination Using Modif...

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4th International Conference on Swarm, evolutionary, and Memetic Computing (SEMCCO)

作者： Moirangthem, Joymala Panigrahi, Bijaya Ketan Krishnanand, K. R. Panda, Sanjib Kumar SOA Univ Bhubaneswar Orissa India Indian Inst Technol Delhi India Natl Univ Singapore Singapore 117548 Singapore

ISBN: (纸本)9783319037554;9783319037561

In real power system, the system may be subjected to operate in different network topologies due to single line outage contingencies, network reconfiguration and maintenance. These changes in the network would lead to operational inconsistency of directional overcurrent relays. To overcome this problem, a set of new coordination constraints corresponding to each network topology needs to be formulated. Directional Overcurrent Relays (ODCRs) problem can be formulated as a nonlinear optimization problem and also in addition to nonlinearity, the optimization problem encounter a large number of coordination constraints. This paper presents a modified differential evolution (DE) algorithm to handle such type of Optimal Directional Overcurrent Relays problem. Modified DE computes the optimal time dial setting and pickup current setting in terms of discrete values which collectively minimize the total operating time of the relays. To verify the performance of the proposed method, similar evolutionary computation methods such as the Genetic algorithm (GA) approaches are also implemented using the same database. The proposed method has been verified on 8-bus test system. The results indicate that the proposed method can obtain better results than the method compared in terms of total operating time and convergence performance for both fixed and changed network topologies.

关键词： differential evolution algorithm Directional overcurrent relays Relay coordination Pickup current settings Time dial settings Time multiplier settings

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differential evolution based Trajectory Optimization of a Four Stage Launch Vehicle

Differential Evolution based Trajectory Optimization of a Fo...

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International Conference on Advanced Communication Control and Computing Technologies

作者： Abhirup Ghosh Sudipta Chattopadhyay Dept. of Electronics and Telecommunication Engineering Jadavpur UniversityKolkata India

ISBN: (纸本)9781479939152

This paper presents a methodology based on differential evolution (DE) algorithm to perform trajectory optimization for generating reference trajectory of a four-stage Satellite Launch Vehicle (SLV). For the generation of reference trajectory, suitable objective function has been formulated incorporating parameters and the contribution of these parameters in developing the objective function has been represented by assigning different weighting factors against the different parameters. Moreover, optimal selection of the setting of parameters has also been achieved by proper optimization of the weighting factors. Finally, a comparative study has been presented between the proposed DE based design and the existing GA based design.

关键词： differential evolution algorithm trajectory optimization SL V objective function GA

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An improved IWO-FCM Data Mining algorithm

An improved IWO-FCM Data Mining Algorithm

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26th Chinese Control and Decision Conference (CCDC)

作者： Zhao Xiaoqiang Zhou Jinhu Gansu Mfg Informationizat Engn Technol Res Ctr Lanzhou 730050 Peoples R China

ISBN: (纸本)9781479937066

The FCM algorithm based on invasive weed optimization algorithm (IWO-FCW) has stronger global optimization ability and higher clustering precision than the basic FCM algorithm, but the IWO-FCW algorithm exists some questions that the convergence become slow and the clustering precision is not high for high and complex testing data sets. So an improved IWO-FCM algorithm is proposed in this paper. This algorithm uses the chaos sequence to initialize the initial population in order to improve initial solution (seed) quality, then the crossover, mutation and part selection operation of the differential evolution algorithm are applied in the spatial distribution and selection process of IWO-FCM algorithm to keep the population diversity and enhance global optimization ability. By testing multiple high-dimensional data sets, the simulation results show that the proposed algorithm has faster convergence speed and higher optimization precision than FCM algorithm and IWO-FCM algorithm.

关键词： Data mining IWO-FCM Chaos differential evolution algorithm

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Two enhanced differential evolution algorithms for job shop scheduling problems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2012年第10期50卷 2757-2773页

作者： Wisittipanich, W. Kachitvichyanukul, V. Asian Inst Technol Klongluang Thailand

This paper proposes two new differential evolution algorithms (DE) for solving the job shop scheduling problem (JSP) that minimises two single objective functions: makespan and total weighted tardiness. The proposed algorithms aim to enhance the efficiency of the search by dynamically balancing exploration and exploitation ability in DE and avoiding the problem of premature convergence. The first algorithm allows DE population to simultaneously perform different mutation strategies in order to extract the strengths of various strategies and compensate for the weaknesses of each individual strategy to enhance the overall performance. The second algorithm allows the whole DE population to change the search behaviour whenever the solutions do not improve. This study also introduces a modified local mutation operation embedded in the two proposed DE algorithms to promote exploitation in different areas of the search space. In addition, a local search technique, called Critical Block (CB) neighbourhood, is applied to enhance the quality of solutions. The performances of the proposed algorithms are evaluated on a set of benchmark problems and compared with results obtained from an efficient existing Particle Swarm Optimisation (PSO) algorithm. The numerical results demonstrate that the proposed DE algorithms yield promising results while using shorter computing times and fewer numbers of function evaluations.

关键词： evolutionary algorithm differential evolution algorithm job shop scheduling

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Study on Reactive Power Optimization Based on Improved Ant Colony algorithm

Study on Reactive Power Optimization Based on Improved Ant C...

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2012 International Conference on Computer Science and Artificial Intelligence(CSAI 2012)

作者： Xiuqing Li Fang Han Chengru Wang Tingting Fang Xiaozhe Xing Department of Electrical Engineering Northeast Dianli University Northwest China Grid Company Limited

For solving the reactive power optimization problems, the ant colony algorithm in combination with artificial fish-swarm and differential evolution algorithms (FDEACO) was presented. Inspired by feeding, clustering and rear-end behaviors of Artificial fish-swarm algorithms, on the basic of the ant colony algorithm, I applied the rear-end behavior of the Artificial fish-swarm algorithm to modify the solution of a feasible region searched by ant colony. The velocity of convergence to the optimal solution is accelerated. In the mechanism of pheromone update, the divergence of the differential evolution algorithm was introduced. A random disturbance is added, and the possibility of getting into local optimum is reduced. By reactive power optimization of IEEE 30-bus standard testing systems, and compared with other algorithms, analyzing the results show that the proposed algorithm is efficient and possesses a strong ability of global optimal searching.

关键词： Power Systems reactive power optimization ant colony algorithm artificial fish-swarm algorithm differential evolution algorithm

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