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multi-objective particle swarm optimization of Sensor Distribution Scheme with Consideration of the Accuracy and the Robustness for Deformation Reconstruction

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SENSORS 2019年第6期19卷 1306.-1306.页

作者： Zhao, Feifei Bao, Hong Xue, Song Xu, Qian Xidian Univ Key Lab Elect Equipment Struct Design Minist Educ Xian 710071 Shaanxi Peoples R China Chinese Acad Sci Xinjiang Observ Natl Astron Observ Urumqi 830011 Peoples R China

For the inverse finite element method (iFEM), an inappropriate scheme of strain senor distribution would cause severe degradation of the deformation reconstruction accuracy. The robustness of the strain-displacement transfer relationship and the accuracy of reconstruction displacement are the two key factors of reconstruction accuracy. Previous research studies have been focused on single-objective optimization for the robustness of the strain-displacement transfer relationship. However, researchers found that it was difficult to reach a mutual balance between robustness and accuracy using single-objective optimization. In order to solve this problem, a bi-objective optimal model for the scheme of sensor distribution was proposed for this paper, where multi-objective particle swarm optimization (MOPSO) was employed to optimize the robustness and the accuracy. Initially, a hollow circular beam subjected to various loads was used as a case to perform the static analysis. Next, the optimization model was established and two different schemes of strain sensor were obtained correspondingly. Finally, the proposed schemes were successfully implemented in both the simulation calculation and the experiment test. It was found that the results from the proposed optimization model in this paper proved to be a promising tool for the selection of the scheme of strain sensor distribution.

关键词： inverse finite element method multi-objective particle swarm optimization deformation reconstruction transfer relationship optimal model

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An Improved multi-objective particle swarm optimization Routing on MANET

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Computer Systems Science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using multi-objective particle swarm optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第4期15卷 300-311页

作者： Shan CHENG Min-you CHEN Rong-jong WAI Fang-zong WANG College of Electrical Engineering and New Energy China Three Gorges University State Key Laboratory of Power Transmission Equipment & System Security and New Technology Department of Electrical Engineering and Fuel Cell Center Yuan Ze University

This paper deals with the optimal placement of distributed generation(DG) units in distribution systems via an enhanced multi-objective particle swarm optimization(EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational constraints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been integrated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is employed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage stability. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and locations of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.

关键词： Distributed generation multi-objective particle swarm optimization Optimal placement Voltage stability index Power loss

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Population recombination strategies for multi-objective particle swarm optimization

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SOFT COMPUTING 2017年第16期21卷 4693-4705页

作者： Zheng, Li Ming Wang, Qiang Zhang, Sheng Xin Zheng, Shao Yong Univ Jinan Sch Informat Sci & Technol Dept Elect Engn Guangzhou 510632 Guangdong Peoples R China Sun Yat Sen Univ Dept Elect & Commun Engn Guangzhou 510006 Guangdong Peoples R China

multi-objective particle swarm optimization algorithm (MOPSOs) has been found to exhibit fast convergence speed but with high probability to fall into local optimum. To overcome this shortcoming, a population recombination strategy is combined with a new mutation strategy to strengthen the ability to jump out of local optimum. From the investigation conducted, it can be found that, when the MOPSO falls into local optimum, the population will stop producing effective particles to update the archive. Population recombination strategy, which utilizes the information of the best variable found so far to construct the new population. This can increase the probability for population to approach the Pareto optimal front, while additional mutation operation can enhance the diversity of population. Experimental study on the bi-objective and three-objective benchmark problems shows that the MOPSO based on proposed strategies is superior to previous multi-objective algorithms in the literature.

关键词： multi-objective particle swarm optimization Population recombination strategies Mutation strategy Local optimum

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Trajectory optimization for manipulators based on external archives self-searching multi-objective particle swarm optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第2期236卷 1188-1201页

作者： Liu, Youyu Zhang, Xuyou Minist Educ Key Lab Adv Percept & Intelligent Control High En Wuhu Peoples R China Anhui Polytech Univ Sch Mech Engn Wuhu 241000 Peoples R China

In order to improve the quality of the non-inferior solutions obtained by multi-objective particle swarm optimization (MOPSO), an improved algorithm called external archives self-searching multi-objective particle swarm optimization (EASS-MOPSO) was proposed and applied to a multi-objective trajectory optimization problem for manipulators. The position curves of joints were constructed by using quartic B-splines;the mathematical models of time, energy and jerk optimization objectives for manipulators were established;and the kinematic constraints of joints were transformed into the constraints of the control vertexes of the B-splines. A self-searching strategy of external archives to make non-inferior solutions have the ability to search the surrounding hyperspace was explored, and a diversity maintaining strategy of the external archives was proposed. The results of several test functions by simulation show that the convergence and diversity of the proposed algorithm are better than those of other 4 selected algorithms;the results of the trajectory optimization problem for manipulators by simulation show that the convergence, diversity and time consumption of the proposed algorithm are significantly better than those of non-dominated sorting genetic algorithm.

关键词： External archive multi-objective particle swarm optimization manipulator trajectory optimization

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Prepositioning emergency earthquake response supplies: A new multi-objective particle swarm optimization algorithm

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APPLIED MATHEMATICAL MODELLING 2016年第9-10期40卷 5183-5199页

作者： Mohammadi, R. Ghomi, S. M. T. Fatemi Jolai, F. Amirkabir Univ Technol Dept Ind Engn 424 Hafez Ave Tehran Iran Univ Tehran Coll Engn Dept Ind Engn Tehran Iran

The development of an efficient emergency response plan to provide critical daily supplies to affected people facilitates efficient relief operations. In this study, we propose a multi objective stochastic programming model for developing an earthquake response plan, which integrates pre-and post-disaster decisions. This three-objective model attempts to maximize the total expected demand coverage, to minimize the total expected cost, and to minimize the difference in the satisfaction rates between nodes. We develop a new multi-objective particle swarm optimization (MOPSO) algorithm to solve this model. Genotype-phenotype-based binary particle swarm optimization (PSO) and continuous PSO are designed to deal with the binary location and other continuous decision variables, respectively. A new strategy is employed to select two types of guides in order to enhance the search ability. Furthermore, a new adaptive inertia weight strategy and two mutation operators are used to ensure that the diversity is sufficient and to regulate the exploration and exploitation capacities, respectively. We present an illustrative real-world case study and some randomly generated instances for computational applications. The results obtained by the proposed MOPSO algorithm were compared with those obtained using a modified time-variant MOPSO, the non-dominated sorting genetic algorithm, strength Pareto evolutionary algorithm, and the exact solution method where possible. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Earthquake response Facility location multi-objective particle swarm optimization Prepositioning emergency supplies Stochastic programming

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Adaptive projection plane and reference point strategy for multi-objective particle swarm optimization

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ALEXANDRIA ENGINEERING JOURNAL 2024年 105卷 381-401页

作者： Zhang, Yansong Liu, Yanmin Zhang, Xiaoyan Song, Qian Yang, Jie Guizhou Univ Sch Math & Stat Guiyang 550025 Peoples R China Zunyi Normal Coll Zunyi 563002 Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China

Achieving a balance between convergence and diversity and their mutual enhancement is a complex task in the process of algorithm improvement. This is crucial because it is directly related to the effectiveness of the algorithm in obtaining accurate and uniformly distributed Pareto frontiers. Although significant progress has been made in particle swarm algorithms, exploring new approaches is necessary. In this paper, we construct a projection plane (projection line in 2D) based on the extreme values of the non-dominated solutions, select a set of uniform reference points on the projection plane, and then project the non-dominated solutions onto the constructed projection plane to form projection points. The reference points and projection points on the projection plane are thus utilized to guide the updating of the population as well as the maintenance of the external archive, a strategy that enhances the algorithm's global exploration and local exploitation capabilities. Secondly, we aggregate the target values of particles into a single scalar value and combine the idea of particle fusion to design a scheme for the particle selection of individual optimal particles. This paper further improves the algorithm's overall performance by using the information between populations to select individual optimal particles. Lastly, it is evaluated against a number of multi-objective algorithms that are currently in use and perform well on 22 test problems. The findings demonstrate that the algorithm this paper proposes performs better when solving multi-objective problems.

关键词： multi-objective particle swarm optimization Projection plane Reference point Clustering

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Adaptive Re-planning of AUVs for Environmental Sampling Missions: A Fuzzy Decision Support System Based on multi-objective particle swarm optimization

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2018年第2期20卷 650-671页

作者： Zhou, Hexiong Zeng, Zheng Lian, Lian Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai Peoples R China Harbin Engn Univ Coll Automat Harbin Heilongjiang Peoples R China Shanghai Jiao Tong Univ Inst Oceanol Shanghai Peoples R China

This paper presents the first attempt at formulating the autonomous underwater vehicles (AUVs) adaptive sampling problem considering a spatiotemporal ocean environment and energy constraints. A path re-planning system that applies fuzzy logic in decision-making is then proposed to increase the sampling efficiency and reduce power consumption to complete the mission. The goal of adaptive sampling is to plan the trajectories of gliders or AUVs to collect ocean measurements located in areas most relevant to the phenomena under investigation. However, in an actual marine environment, common conditions exist under which AUVs need to adjust their routes as the phenomena change over time. These conditions also require the monitoring of AUV energy consumption under the effect of currents that vary in space and time. This two-objective optimization problem is solved using the multi-objective particle swarm optimization algorithm to produce a set of solutions. A new fuzzy comprehensive evaluation (FCE) method, based on decision-making using fuzzy logic, is then applied to select the optimum route from the solutions by adapting and regenerating trajectories according to the ocean forecast system, analysing different scenarios, and including constraints of the total operational time and energy restrictions. Several Monte Carlo simulations are conducted to evaluate the performance and robustness of strategies determined via FCE for various scenarios.

关键词： Path re-planning Fuzzy decision-making Autonomous underwater vehicle Adaptive sampling multi-objective particle swarm optimization Ocean forecast system

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A multi-objective particle swarm optimization Based Algorithm for Primary User Emulation Attack Detection

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期117卷 867-886页

作者： Fassi Fihri, Wassim El Ghazi, Hassan Abou El Majd, Badr Natl Inst Posts & Telecommun INPT STRS Lab Rabat Morocco Mohammed V Univ LMSA Lab FSR Rabat Morocco

The cognitive radio network (CRN) has been proposed to overcome the spectrum scarcity and the massive demand on the radio frequencies through efficient utilization and smart management of the free channels. Using a spectrum sensing process, the secondary users (SUs) share the spectrum of the primary users (PUs) without causing any interference to these latter. One of the main threats affecting the channel utilization in CRN is the primary user emulation (PUE) attack. The PUE attack utilizes a sophisticated technique to craft the same signal of a legitimate PU to gain unauthorized access to the unused channels, forcing the SUs to immediately free up vacant spectrum space, resulting in a denial of service (DoS), degradation of service, and causing a noticeable impact on CRN performance. To circumvent this attack, the anchor nodes must accurately estimate the coordinates of the PU signal source. In the literature, most of the localization of unknown signal source, in our case the PU/PUE, are based on the ranging schemes which measure the distance between the blind node and the anchors. Those anchors are aware of their location and situated in optimized positions to the signal source in order to permit an accurate PU/PUE position detection. The detection rate depends tightly on the distance separating the anchors to the signal source as well as the signal-to-noise ratio (SNR). In this paper, we demonstrate the impact of the distance on the detection error while highlighting the particle swarm optimization's (PSO) advantage in optimizing the anchors positioning. Furthermore, we illustrate the SNR impact on the probability of detection, particularly in the situation of low SNR and the attacker in the vicinity to a real PU. The main contribution in this work is the proposition of an approach with the aim of protecting an area containing more than a single PU with a limited number of anchor nodes while providing a higher detection rate to stop any eventual PUE attack. This

关键词： Cognitive radio Primary user emulation Deny of service Localization error Received signal strength Trilateration Signal-to-noise ratio particle swarm optimization multi-objective particle swarm optimization

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Dynamic Stochastic Resonance Based Diffusion-Weighted Magnetic Resonance Image Enhancement Using multi-objective particle swarm optimization

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JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2016年第6期36卷 891-900页

作者： Singh, Munendra Sharma, Neeraj Verma, Ashish Sharma, Shiru Banaras Hindu Univ Indian Inst Technol Sch Biomed Engn Varanasi 221005 Uttar Pradesh India Banaras Hindu Univ Inst Med Sci Dept Radiodiag & Imaging Varanasi 221005 Uttar Pradesh India

Diffusion weighted (DW) magnetic resonance (MR) imaging maps the diffusion process of water in the tissues. DW-MR image is useful to probe the tissue microstructure, but suffers from inherent low signal to noise ratio and poor contrast. Dynamic stochastic resonance (DSR) utilizes the noise to enhance the low contrast image where the quality of the processed image depends on the bistability parameters of DSR and the number of iterations. This paper presents an approach that optimally finds the bistability parameters and number of iterations for the maximization of competitive image quality indices: contrast enhancement factor and mean opinion score using multi-objective particle swarm optimization. The proposed particle swarm optimization optimized DSR algorithm has been tested on 40 DW-MR brain images of different subjects. The quantified results show average contrast enhancement factor, 1.603 and average perceptual quality measure, 9.508. These values are significantly higher than image quality indices of original image, the images that are produced by conventional enhancement methods and filtering followed by enhancement methods.

关键词： Dynamic stochastic resonance Optimum contrast multi-objective particle swarm optimization Diffusion-weighted MR image Contrast enhancement factor Mean opinion score

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