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Efficient swarm intelligent algorithm for power control game in cognitive radio networks

IET COMMUNICATIONS 2013年第11期7卷 1089-1098页

作者： Kuo, Yonghong Yang, Jianghong Chen, Jian Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China

Cognitive radio networks (CRNs) are applied to solve spectrum scarcity. In this study, the authors propose an efficient power control game to improve its performance based on outage probability of primary user in a spectrum-underlay CRN. The interference threshold deduced from outage probability and normalised signal to interference plus noise ratio are used to develop a novel non-linear pricing function, which is a key element of obtaining Pareto improvement in non-cooperative power control game. In addition, an efficient swarm intelligent algorithm originated from eco-group activities is designed in detail to accelerate the convergence speed and improve the energy-efficiency. Theoretical analysis and simulation results are presented to prove the effectiveness and superiority of the proposed power control game.

关键词： cognitive radio convergence error statistics game theory power control pricing radiofrequency interference swarm intelligence energy-efficiency improvement convergence speed eco-group activities noncooperative power control game Pareto improvement nonlinear pricing function normalised signal to interference plus noise ratio interference threshold spectrum-underlay CRN primary user outage probability spectrum scarcity cognitive radio networks swarm intelligent algorithm

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Image Forgery Detection Using Segmentation and swarm intelligent algorithm

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Wuhan University Journal of Natural Sciences 2017年第2期22卷 141-148页

作者： ZHAO Fei SHI Wenchang QIN Bo LIANG Bin School of Information Renmin University of China Beijing 100872 China

Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform （SIFT）-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region.

关键词： copy-move forgery detection scale invariant features transform (SIFT) swarm intelligent algorithm particle swarm optimization

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Drilling path optimization based on swarm intelligent algorithm

Drilling path optimization based on swarm intelligent algori...

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IEEE International Conference on Robotics and Biomimetics (ROBIO 2006)

作者： Zhu, Guang Yu Fuzhou Univ Coll Mech Engn & Automat Fuzhou Fujian Province Peoples R China

ISBN: (纸本)9781424405701

Drilling path optimization is the key problem in holes machining. This paper presents a swarm intelligent approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, the algorithm is improved by adopting the method of generating the stop evolution particle over again to get the ability of convergence on the global optimization solution. And the operators are improved by establishing the order exchange unit and the order exchange list to satisfy the need of integer coding in drilling path optimization. The experimentations indicate that the improved algorithm has the characteristics of easy realization, fast convergence speed, and better global converging capability. Hence the new PSO can play a role in solving the problem of drilling path optimization.

关键词： driling path optimization swarm intelligent algorithm PSO algorithm convergence

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Applications of swarm intelligent algorithm in Economic Model

Applications of Swarm Intelligent Algorithm in Economic Mode...

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2018 5th International Conference on Electrical & Electronics Engineering and Computer Science(ICEEECS 2018)

作者： Shi Yaping Guangdong University of Science and Technology

swarm intelligent optimization algorithm is a method which completes the optimization algorithm through information interaction between some or the entire individuals based on random search optimization. This paper puts forward the optimization method of micro grid economic model based on the particle algorithm through the research on swarm intelligent algorithm. In the micro grid economic model, this paper calculates the optimal economic dispatch results of micro grid system. The calculation results are compared and analyzed, which has realistic meaning to the design and planning of the micro grid energy management.

关键词： swarm intelligent algorithm economic model

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Trajectory Optimization of the Exploration of Asteroids Using swarm intelligent algorithms

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清华大学学报（英文版） 2009年第z1期14卷 7-11页

作者： ZHU Kaijian LI Junfeng BAOYIN Hexi School of Aerospace Tsinghua UniversityBeijing 100084China

As a typical NP-HARD problem,trajectory optimization has become the hot point in aerospace *** the limitation of classical optimization algorithms,variant global optimization algorithms for trajectory optimization are applied in an extensive *** on the parameter optimization of low thrust and impulse maneuver,this paper investigates the difference evolution(DE),particle swarm optimization(PSO),and genetic algorithm(GA)algorithms and generates two hybrid *** proves to be superior to other non-hybrid algorithms in trajectory optimization *** hybrid algorithm of PSO and DE can improve the optimization performance of DE,which is validated by two given *** results in this paper indicate the validity and feasibility of the hybrid algorithms.

关键词： trajectory optimization low-thrust swarm intelligent algorithm exploration

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Improving building energy consumption prediction using occupant-building interaction inputs and improved swarm intelligent algorithms

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JOURNAL OF BUILDING ENGINEERING 2023年 73卷

作者： Zhang, Chengyu Ma, Liangdong Han, Xing Zhao, Tianyi Dalian Univ Technol Inst Bldg Energy Dalian Peoples R China PCI Technol Grp Guangzhou Peoples R China

Building energy consumption prediction is important for sustainable building and city construction. However, traditional data-driven prediction methods weakly consider real-time interactions among humans, energy systems, and the environment, which reduces forecasting accuracies. In addition, compared to traditional machine learning algorithms, more suitable algorithms may be required to improve forecasting accuracy and accelerate convergence speed. In this study, an improved energy-consumption prediction system is proposed that includes input system modification and algorithm improvement. For inputs, the probability of sockets, lighting, and air-conditioning related behaviors are included as inputs. Compared with previous studies, the duration of behaviors instead of the number of behavior occurrences is used in this study to solve the behavior probability, which can more accurately reflect occupant-building interactions during forecasting. For algorithms, swarm intelligence algorithms with circle mapping and selfadaptive weight adjustment are used, retaining the diversity of the population and optimizing the weight of the neural network, thereby resulting in better algorithm performance. Results showed that the case using occupant-building interaction inputs and improved swarm intelligence algorithms had the best performance. The coefficients of determination are 0.9588-0.9901, and the mean absolute percentage errors are 4.44%-11.60%. When using the same algorithms and optimization, the modified inputs can improve the prediction performance by 9.15%-21.59%. Also, when using the same inputs, the improved algorithms can improve the precision performance by 5.46%-22.37%. Also, this study discusses the priority of selecting a combination of inputs and algorithms, which will help users to identify different inputs and algorithms when there are different data collection conditions and accuracy requirements. All cases in this study had coefficients of determination above

关键词： Building energy consumption Occupant-orientated input system swarm intelligent algorithm Circle chaotic mapping Self-adaptive weight adjustment

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Bi-objective scenario-guided swarm intelligent algorithms based on reinforcement learning for robust unrelated parallel machines scheduling with setup times

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swarm AND EVOLUTIONARY COMPUTATION 2023年 80卷

作者： Wang, Bing Feng, Kai Wang, Xiaozhi Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

This paper addresses an uncertain unrelated parallel machine scheduling problem (UPMSP) with setup times, which is referred to the scenario UPMSP since uncertain processing times are described by a set of discrete scenarios. The bi-objective robust optimization formulation is established. Two objectives are to minimize the mean makespan and the worst-case makespan across all scenarios, which reflect solution optimality and solution robustness respectively. The contribution of this paper is three-fold. First, we propose the bi-objective robust optimization formulation under discrete scenarios for uncertain UPMSP. Second, two versions of swarm intelligent algorithms are developed by combining fruit fly optimization algorithm (FOA) framework and scenarioguided local search, which are performed based on two problem-specific neighborhood structures. The learning-scenario neighborhood structure is constructed by selecting single scenario using reinforcement learning. The united-scenario neighborhood structure is constructed by collecting all discrete scenarios. Third, an experiment was conducted to compare two developed algorithms with the state-of-the-art alternative algorithms. The computational results show that the developed algorithms are identically better than possible alternatives in terms of multi-objective metrics. Moreover, it is shown that the FOA algorithm with learning-scenarioneighborhood smell search is advantageous for the discussed problem.

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intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

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IEEE ACCESS 2020年 8卷 63993-64001页

作者： Liu, Dongbo Fang, Yu Wang, Haibin Xihua Univ Sch Elect Engn & Elect Informat Chengdu 610039 Peoples R China Yamaguchi Univ Grad Sch Sci & Technol Innovat Yamaguchi 7538511 Japan

Piezoelectric actuators (PAs) require high precision positioning for the applications of micro electrical mechanical systems, but it exhibits hysteresis nonlinearity which deteriorates positioning accuracy if no proper compensation is given. Hysteresis nonlinear modeling of PAs is a prime choice for hysteresis compensation. This paper proposes a novel intelligent positioning control algorithm based on Bouc-Wen (BW) model for the compensation of a bi-morph type piezoelectric actuator (PA) suffering rate-dependent hysteresis. A region based mixed-species swarm optimization (RMSO) algorithm is proposed for BW modeling to capture the dynamic nonlinearity of a piezoelectric actuator which exhibits rate-dependent hysteresis. Results of numerical simulations have been disclosed to illustrate the performance enhancement of RMSO over classical algorithm while they are applied to the parameter fitting problem of BW model for experimentally acquired datasets. An model based adaptive Fuzzy neural network (Fuzzy-NN) controller of PA is utilized to compensate the hysteresis for the positioning tracking control. Experimental results also illustrate the good performance of the proposed RMSO-BW based control scheme for the hysteresis compensation control of the PA.

关键词： Hysteresis Mathematical model Biological system modeling Piezoelectric actuators Optimization Adaptation models Computational modeling Piezoelectric actuator swarm intelligent algorithm hysteresis nonlinearity Bouc-Wen modeling intelligent control

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A Novel Magnetic Spiral Capsule Endoscope Localization Method Based on an Improved Artificial Bee Colony algorithm

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IEEE SENSORS JOURNAL 2024年第2期24卷 1740-1750页

作者： Ye, Bo Hu, Jinping Zha, Wei Wang, Bo Gao, Qiyuan Fang, Guo Dwivedi, Amit Krishna Liu, Sheng Hubei Univ Sch Artificial Intelligence Wuhan 430062 Peoples R China Manchester Metropolitan Univ Dept Engn Manchester M15 6BH England Univ Warwick Sch Engn Coventry CV4 7AL England Wuhan Univ Sch Power & Mech Engn Wuhan Peoples R China

A novel localization method for wireless capsule endoscope (WCE) based on an improved artificial bee colony (IABC) algorithm is presented. This work contributes to improving the two key aspects of the approach, i.e., optimizing the parameters and improving the search strategy. The experimental results achieved in this work are compared with the Levenberg-Marquardt (LM) algorithm, artificial bee colony (ABC) algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Compared with the GA algorithm, ABC algorithm and PSO algorithm, the IABC algorithm achieved significant improvement in terms of the convergence speed, convergence time and running time, and average relative error. For instance, the average relative error of the IABC algorithm was reduced by 3.48%, 1.47%, 2.79%, and 1.97%, compared with the GA algorithm, ABC algorithm, PSO algorithm, and LM algorithm, respectively. In this work, experiments were also performed to validate the feasibility and accuracy of the proposed algorithm. Overall, promising results were achieved in this work and outcomes play a significant role in realizing WCE closed-loop active control, biopsy, and administration in the future.

关键词： Location awareness Endoscopes Magnetic sensors Magnetostatics Permanent magnets Spirals Wireless sensor networks Improved artificial bee colony (IABC) algorithm magnetic localization magnetic sensor array swarm intelligent algorithm wireless capsule endoscope (WCE)

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Truss optimization with frequency constraints using the medalist learning algorithm

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STRUCTURES 2023年 55卷 1-15页

作者： He, Sheng-Xue Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

Though the truss optimization with frequency constraints is very complicated due to its high nonlinear property, the importance and necessity of truss optimization in the real engineering design render it attractive for many researchers. This paper proposed a new heuristic algorithm inspired by the observation of the learning behavior of individuals in a group for the truss optimization with frequency constraints. The new swarm intelligent heuristic algorithm called the medalist learning algorithm has a concise implementation procedure composed of two key operations: identifying the medalists and conducting the individual learning. The learning period is divided into predefined learning stages. In each learning stage, a learning efficiency is generated using a learning function originated from the natural learning function. The key operations mentioned above are repeated in each learning stage. For an excellent performer called medalist, to conduct individual learning is to carry out self-learning trials;but for a common learner, to conduct individual learning is to learn from his/her past experi-ence and imitate the medalists. Six well-known truss optimization problems were employed to verify the effi-ciency of the new algorithm. The results obtained by the medalist learning algorithm were compared with those previously reported in literature. The new algorithm outperforms the others in terms of the best and average weights. The computational cost measured by the number of the function estimations of the structure for the new algorithm to obtain the best design usually is in the middle of the reported costs. In some cases, the new algo-rithm has the lowest worst weight and smallest number of function estimations.

关键词： Truss optimization Medalist learning algorithm Frequency constraint Size and layout optimization swarm intelligent algorithm

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