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Optimal control of mobile monitoring agents in immune-inspired wireless monitoring networks

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2011年第6期34卷 1818-1826页

作者： Liu, Wenjia Chen, Bo Michigan Technol Univ Dept Mech Engn Engn Mech Dept Elect & Comp Engn Houghton MI 49931 USA

This paper studies optimal control of mobile monitoring agents in artificial-immune-system-based (AIS-based) monitoring networks. In AIS-based structural health monitoring (SHM) networks, the active structural health monitoring is performed by a group of mobile monitoring agents equipped with damage pattern recognition algorithms. The mobile monitoring agents mimic immune cells in the natural immune system and patrol a structure to detect damage patterns using their receptors (feature vectors), damage pattern recognition algorithms, and the dynamic response data of the structure. The optimal control of mobile monitoring agents includes agent generation and distribution. The generation of mobile monitoring agents is optimized to minimize the response time for the mobile monitoring agents to diagnose structural damage in a sub-network and maximize the average affinity of monitoring agents' receptors to the damaged sensor data feature vector. The objective functions for distributing mobile monitoring agents are to increase the detection probability and extend network life by balancing energy consumption of sensor nodes in the network. The presented optimization algorithms are developed using multi-objective genetic algorithms. The impact of the algorithm parameters on the performance of the algorithm is also investigated. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm Mobile monitoring agent Artificial immune system

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A multi-objective Robust Optimization Model for Customized Bus Routes

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第4期22卷 2359-2370页

作者： Ma, Changxi Wang, Chao Xu, Xuecai Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Huazhong Univ Sci & Technol Sch Civil Engn & Mech Wuhan 430074 Peoples R China

Various customized bus route optimization methods based on certain conditions have been applied to the actual route optimization problems, but the actual operation process of customized buses mostly lies in an uncertain condition. In this paper, a three-stage hybrid coding method based on NSGA-II algorithm was proposed to deal with customized bus route optimization under uncertain condition. Firstly, with the objective of minimizing passenger travel time and customized bus carbon emission, a robust optimization model was constructed. Second, with the Bertsimas-Sim robust optimization theory, the robust peer-to-peer transformation was performed on the robust model with uncertain parameters. Finally, the practical issue including three customized bus parking lots and 20 boarding and alighting stations were solved to verify the rationality of the model and algorithm. Compared with the hybrid algorithm based on K-means and multi-objective genetic algorithm, this method reveals not only better solution results, but saves 42.11% of computing time. The results are of great value for exploring customized bus route optimization methods and improving the efficiency of customized bus operations.

关键词： Public transportation Urban areas Optimization methods Encoding genetic algorithms Customized bus multi-objective genetic algorithm route optimization robust model

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A multi-objective particle swarm for a flow shop scheduling problem

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2007年第1期13卷 79-102页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Univ Tehran Fac Engn Dept Ind Engn Tehran Iran

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective particle swarm (MOPS), exploiting a new concept of the Ideal Point and a new approach to specify the superior particle's position vector in the swarm, is designed and used for finding locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOPS performs better than the genetic algorithm, especially for the large-sized problems.

关键词： bi-criteria flow shop scheduling problem permutation flow shop multi-objective particle swarm multi-objective genetic algorithm

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Investigation of Tool Chatter Features at Higher Metal Removal Rate Using Sound Signals

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ACOUSTICS AUSTRALIA 2020年第1期48卷 141-148页

作者： Gupta, Pankaj Singh, Bhagat Jaypee Univ Engn & Technol Guna MP India

Chatter-free turning has been a matter of concern for engineers. Improper selection of cutting parameters leads to regenerative chatter and loss of productivity. In the present work, a methodology has been proposed to select an appropriate combination of input cutting parameters for stable turning with improved metal removal rate (MRR). Chatter signals generated during the turning of Al 6061 are acquired using a microphone and processed using local mean decomposition signal processing technique. Thereafter, these decomposed signals have been analyzed in order to extract tool chatter features. Prediction models of chatter indices and MRR have been developed using response surface methodology. Moreover, these prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher MRR. Finally, obtained stable range has been validated by performing more experiments.

关键词： Tool chatter Local mean decomposition Chatter indices Nakagami distribution RSM multi-objective genetic algorithm

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multi-objective optimization of collation delay and makespan in mail-order pharmacy automated distribution system

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期83卷 475-488页

作者： Mei, Kevin Li, Debiao Yoon, Sang Won Ryu, Jong-hynn Fuzhou Univ Fuzhou 350002 Fujian Peoples R China SUNY Binghamton Binghamton NY USA Hongik Univ Sejong South Korea

In this research, the collation delay (CD) and makespan minimization problem in a mail-order pharmacy automated distribution (MOPAD) system are studied. The MOPAD systems, which are integrated with pharmaceutical auto-dispenser machines, auto-packer machines, and conveyor, are utilized to fulfill the increasing prescription demand in recent years. The motivation of this research is derived from the practical deadlock problem in a MOPAD system of the central fill pharmacies (CFP). Most of the customer orders consist of multiple medications, which need to be collated together before being packaged and shipped. The CD is defined as the fulfillment completion time difference between the first and last medications within the same order, which is a critical factor of the MOPAD systems throughput. When CD is minimized, the makespan often increases. Therefore, alternative scheduling solutions are often needed to balance the CD and makespan in the MOPAD system. This paper presents the trade-off solutions between minimizing CD and the makespan. Three multi-objective genetic algorithms with a three-tuples chromosome design, including Vector Evaluated genetic algorithm (VEGA), multi-objective genetic algorithm (MOGA), and non-dominated sorted genetic algorithm-II (NSGA-II), are implemented and compared under various system settings. Compared to the current implemented longest processing time (LPT) heuristic, three multi-objective genetic algorithms save the CD by 95.67 %, but only increase the makespan by 5.62 % on average. The results also show that the NSGA-II provided the best frontier.

关键词： Collation delay minimization Mail-order pharmacy automated distribution system multi-objective genetic algorithm Three-tuple chromosome

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Direct interval forecasting of wind speed using radial basis function neural networks in a multi-objective optimization framework

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NEUROCOMPUTING 2016年 205卷 53-63页

作者： Zhang, Chi Wei, Haikun Xie, Liping Shen, Yu Zhang, Kanjian Southeast Univ Sch Ahtomat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Jiangsu Peoples R China

Point predictions of wind speed can hardly be reliable and accurate when the uncertainty level increases in data. Prediction intervals (PIs) provide a solution to quantify the uncertainty associated with point predictions. In this paper, we adopt radial basis function (RBF) neural networks to perform interval forecasting of the future wind speed. A two-step method is proposed to determine the RBF connection weights in a multi-objective optimization framework. In the first step, the centers of the RBF are determined using the K-means clustering algorithm and the hidden-output weights of the RBF are pre-trained using the least squares algorithm. In the second step, the hidden-output weights are further adjusted by the non-dominated sorting genetic algorithm-II (NSGA-II), which aims at concurrently minimizing the width and maximizing the coverage probability of the constructed intervals. We test the performance of the proposed method on three real data sets, which are collected from different wind farms in China. The experimental results indicate that the proposed method can provide higher quality PIs than the conventional multi-layer perceptron (MLP) based methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Prediction interval Radial basis function neural network multi-layer perceptron multi-objective genetic algorithm Wind speed

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A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem

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ENGINEERING OPTIMIZATION 2007年第8期39卷 877-898页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Rabbani, M. Univ Tehran Dept Ind Engn Tehran Iran

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.

关键词： mixed-model assembly line multi-objective sequencing problem multi-objective particle swarm tabu search multi-objective genetic algorithm

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multi-objective optimization for actuator and sensor layouts of actively controlled 3D buildings

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JOURNAL OF VIBRATION AND CONTROL 2013年第6期19卷 942-960页

作者： Cha, Young-Jin Kim, Yeesock Raich, Anne M. Agrawal, Anil K. CUNY City Coll New York NY 10031 USA Worcester Polytech Inst Worcester MA 01609 USA Lafayette Coll Easton PA 18042 USA

This paper investigates the multi-objective optimization of active control systems for vibration control of three-dimensional (3D) high-rise buildings under a variety of earthquake excitations. To this end, a novel multi-objective genetic algorithm is developed through the integration of the best features of a non-dominated sorting II (NS2) genetic algorithm (GA) and an implicit redundant representation (IRR) GA. The proposed NS2-IRR GA finds not only minimum distributions of both actuators and sensors within structures, but also minimum dynamic responses of 3D structures. Linear quadratic Gaussian controllers, hydraulic actuators and accelerometers are used for implementation of active control systems within the 3D buildings. To demonstrate the effectiveness of the proposed NS2-IRR GA, two 3D building models are investigated using finite element methods, including low-and high-rise buildings. It is shown that the proposed NS2-IRR GA is effective in finding not only optimal locations and numbers of both actuators and sensors in 3D buildings, but also minimum responses of the 3D buildings. The simulation also shows that the control performances of the proposed approach significantly enhance those of the engineering judgment oriented benchmark layout, which is validated by comparisons of each performance using the same number of actuators.

关键词： Actuators multi-objective genetic algorithm optimal placement sensors

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INITIAL-VALUED FIRST-ORDER FUZZY DIFFERENTIAL EQUATION IN BI-LEVEL INVENTORY MODEL WITH FUZZY DEMAND

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MATHEMATICAL MODELLING AND ANALYSIS 2008年第4期13卷 493-512页

作者： Das, B. Mahapatra, N. K. Maiti, M. Jhargram Raj Coll Dept Math Jhargram W Bengal India Panskura Banamali Coll Dept Math Panskura W Bengal India

In the present paper;two methods for the solution of an initial valued first ordered fuzzy differential equation are presented and applied in a fuzzy EOQ model. The constructed model is a bi-level inventory problem involving wholesaler-retailers-customers. The wholesaler buys and sells the item instantaneously to several retailers. In the next level, the retailers sell the units to customers with a time dependent imprecise demand;which introduce the fuzzy nature in the differential equation. The selling price of the item is a step-wise time dependent decreasing function. The fuzzy objectives are transformed into crisp one following fuzzy extension principle and centroid formula. The model is illustrated through Interactive Fuzzy Decision Making (IFDM) and multi objective genetic algorithm (MOGA) and the results from two methods are compared.

关键词： Fuzzy differential equation Bi-level inventory Newsboy problem Interactive fuzzy decision making method multi-objective genetic algorithm

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Method of improving the soil compactor's ride quality based on the optimal negative stiffness structure

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2023年第1-3期91卷 181-207页

作者： Liem, Nguyen Van Ni, Dengke Hubei Polytech Univ Sch Mech & Elect Engn Huangshi 435003 Peoples R China

A new driver's seat suspension designed by the negative-stiffness-structure (NSS) and cab's isolation used by the hydraulic mounts are proposed to enhance the soil compactor's ride quality. A nonlinear vehicle dynamics model that considers the interaction between the wheel/drum and deformable terrains is established to evaluate the NSS's performance. Based on the genetic algorithm, NSS's parameters are optimised to further improve ride quality. The ride quality is evaluated through the displacement and acceleration of the seat and cab in the frequency and time domains. The research results are also verified via the experiment. The results show that the root-mean-square (RMS) and power-spectral-density of the seat and cab accelerations with the optimal NSS are strongly reduced in comparison without NSS. Especially, the RMS seat's acceleration is markedly attenuated by 67.54% under the vehicle working condition. Therefore, the optimal NSS applied on off-road vehicle's seat suspension could enhance the ride quality.

关键词： soil compactors seat suspension system NSS negative stiffness structure ride quality MOGA multi-objective genetic algorithm off-road terrains

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