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A development on multimodal optimization technique and its application in structural damage detection

APPLIED SOFT COMPUTING 2020年 91卷 106264-106264页

作者： Chen, Diancheng Li, Yiyang Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China

In this paper, a heuristic algorithm fusing with niche identification (NIT) and artificial bee colony (ABC) technique is developed to solve multimodal optimization problems, and is then applied for structural damage detection. In order to improve the detection accuracy of the proposed algorithm, the Depth First Search (DFS) is adopted, and a new particle update scheme is proposed to maintain the diversity of particle populations. The effectiveness and robustness of the algorithm for multimodal optimization are demonstrated by the well-known benchmark functions. Case studies on structural damage detection are carried out using ANSYS-powered data. Simulation results show that, even for the contaminated data or extreme damage scenarios (e.g., the adjacent damages), the DFS-based nNIT with ABC technique can lead to a satisfactory result. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multimodal optimization artificial bee colony algorithm Niche identification technique Depth First Search Damage detection

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Group Social Learning in artificial bee colony Optimization algorithm

Group Social Learning in Artificial Bee Colony Optimization ...

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International Conference on Soft Computing for Problem Solving (SocProS 2011)

作者： Sharma, Harish Verma, Abhishek Bansal, Jagdish Chand ABV Indian Inst Informat Technol & Management Gwalior India

ISBN: (纸本)9788132204862

artificial bee colony (ABC) optimization algorithm is a powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. In ABC each bee stores candidate solution;and stochastically modifies its candidate over time, based on the best solution found by neighboring bees,and based on the best solution found by the bee itself. When tested over various benchmark function and real life problems, it has performed better than a few evolutionary algorithms and other search heuristics. ABC, like other probabilistic optimization algorithms, has inherent drawback of premature convergence or stagnation that leads to loss of exploration and exploitation capability. Therefore, in order to balance between exploration and exploitation capability of ABC a new search strategy is proposed. In the proposed strategy, search process in ABC is performed by smaller group of independent swarms of bees. The experiments with 10 test functions of different complexities show that the proposed strategy has better diversity and faster convergence than the basic ABC.

关键词： Numerical Optimization artificial bee colony algorithm Group Social Learning Swarm intelligence Meta-heuristics

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A Survey of Using Swarm Intelligence algorithms in IoT

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SENSORS 2020年第5期20卷 1420-1420页

作者： Sun, Weifeng Tang, Min Zhang, Lijun Huo, Zhiqiang Shu, Lei Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China Nanjing Agr Univ Coll Engn Nanjing 210031 Peoples R China Univ Lincoln Lincoln England

With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn.

关键词： swarm intelligence algorithm ant colony optimization particle swarm optimization artificial bee colony algorithm wireless sensor network UAV

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Sec-IoV: A Multi-Stage Anomaly Detection Scheme for Internet of Vehicles

Sec-IoV: A Multi-Stage Anomaly Detection Scheme for Internet...

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ACM MobiHoc Workshop on Pervasive Systems in the IoT Era (PERSIST-IoT)

作者： Garg, Sahil Kaur, Kuljeet Kaddoum, Georges Gagnon, Francois Kumar, Neeraj Han, Zhu Ecole Technol Super Montreal PQ H3C 1K3 Canada Thapar Inst Engn & Technol Patiala Punjab India Univ Houston Houston TX 77004 USA

ISBN: (纸本)9781450368056

Rapid progress in Intelligent Transportation Systems (ITS) during the last few decades has resulted in the emergence of the Internet of Vehicles (IoV), in which smart vehicles communicate with each other for information sharing. The exponential increase in the number of vehicles, together with an increasing data demands from in-vehicle users, has led to a tremendous growth of upstream data in the IoV infrastructure. However, the highly dynamic topology and distributed nature of vehicular networks exposes the vehicular traffic to higher security risks. To address these challenges, in this paper, "Sec-IoV", a multi-stage model for anomaly detection is specifically proposed for securing vehicle-to-vehicle (V2V) communications in IoV setups. The proposed anomaly detection model comprises of multiple stages: (a) relevant feature set selection, (b) optimization of Support Vector Machine (SVM) parameters, and (c) classification of vehicular traffic into benign and anomalous. The first two stages are expressed using the multi-objective optimization problems, which are iteratively computed using the hybridization of a meta-heuristic approach "artificial bee colony (ABC) Optimization" with a Cauchy based mutation operator. This coupling is referred to as "C-ABC". It improves the local search capability of the optimizer with faster convergence. The last stage of data classification is then performed by employing SVM with a refined set of parameters. For the extensive evaluation of the proposed model, different state-of-the-art models have been executed on OMNET++ and SUMO. The obtained results in terms of the detection rate, accuracy, and false positive rate reflect the effectiveness of the proposed Sec-IoV model against the existing state-of-the-art schemes.

关键词： Anomaly detection artificial bee colony algorithm Feature selection Internet of Vehicles Support Vector Machine

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A Performance Study of Bio-Inspired algorithms in Autonomous Landing of Unmanned Aerial Vehicle

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Procedia Computer Science 2020年 171卷 1449-1458页

作者： Harun Surej Ilango Ramanathan R. Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham– 641112 India

The complete operational automation of fixed wing Unmanned Aerial Vehicle (UAV) involves the autonomous operations across take-off, cruising and landing. Among all these stages the landing stage is the most crucial one. During landing, it is important for the UAV to maintain a constant speed and glide slope to ensure the stability and a successful touchdown on the runway. Also, it is important for a UAV to estimate the accurate point of landing in a minimal amount of time. Embedding Bio-inspiring algorithms in UAV control systems helps in accurate estimation of the landing point in a minimal amount of time. In this research work, the Bio-inspired optimization algorithms Bats Optimization algorithm, Moth Flame Optimization algorithm and artificial bee colony algorithm are used in determining the coordinates (points) of the computed path and to determine the optimal point of landing which ensures the above said parameters are within the operational limits of the UAV. The objective of this research work is to determine the path from the computed points and to find the optimal landing point in a minimal amount of time. The difference between the original points of the actual path and the derived computed points of the estimated path is measured as the error rate. The performance of the algorithms is analyzed in terms of two trade-off parameters, the time taken to compute the landing point and the accuracy in predicting the landing point. The empirical results show that the Moth Flame Optimization algorithm takes less time to compute the optimal point with minimal error among the three optimization algorithms taken up for the study.

关键词： Fixed-wing Unmanned Aerial Vehicles (UAVs) Bio-inspired optimization algorithms Bats Optimization algorithm Moth Flame Optimization algorithm artificial bee colony algorithm

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Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2011年第6期24卷 946-957页

作者： Samanta, Suman Chakraborty, Shankar Jadavpur Univ Dept Prod Engn Kolkata 700032 W Bengal India

Selection of the optimal values of different operating parameters is of utmost importance for enhancing the performance of various non-traditional machining (NTM) processes. The performance measures (responses) of different NTM processes usually include metal removal rate, surface roughness, radial overcut, tool wear rate, heat affected zone, etc. In this paper, artificial bee colony (ABC) algorithm is employed to search out the optimal combinations of different operating parameters for three widely used NTM processes, i.e. electrochemical machining, electrochemical discharge machining and electrochemical micromachining processes. Both the single and multi-objective optimization problems for the considered NTM processes are solved using this algorithm. The results obtained while applying the ABC algorithm for parametric optimization of these three NTM processes are compared with those derived by the past researchers, which prove the applicability and suitability of the ABC algorithm in enhancing the performance measures of the considered NTM processes. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Optimization artificial bee colony algorithm Response Electrochemical machining Electrochemical discharge machining Electrochemical micromachining

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Partial transmit sequences based on artificial bee colony algorithm for peak-to-average power ratio reduction in multicarrier code division multiple access systems

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IET COMMUNICATIONS 2011年第8期5卷 1155-1162页

作者： Taspinar, N. Karaboga, D. Yildirim, M. Akay, B. Erciyes Univ Dept Elect & Elect Engn Kayseri Turkey Erciyes Univ Dept Comp Engn Kayseri Turkey Bozok Univ Dept Elect & Elect Engn Yozgat Turkey

Multicarrier code division multiple access (MC-CDMA) is a very promising system for wireless communication. However, MC-CDMA signals have a high peak-to-average power ratio (PAPR), which causes signal distortion because of the use of a high-power amplifier (HPA) in the transmitter. Partial transmit sequences (PTSs) represent one of the most attractive PAPR reduction methods, but its high computational complexity in finding the optimal phase vector impedes practical implementation. In this paper, we propose a PTS based on an artificial bee colony (ABC) algorithm scheme (ABC-PTS) to reduce the computational complexity of the PTS in the MC-CDMA systems. Simulation results prove that the proposed ABC-PTS scheme shows a significant improvement in PAPR reduction performance, with a low computational complexity. In addition, the bit-error-rate performance of the MC-CDMA with the ABC-PTS and the conventional PTS is compared when the HPA and the linear amplifier are used.

关键词： sequences high-power amplifier partial transmit sequence peak-to-average power ratio reduction Optimisation techniques HPA error statistics bit error rate artificial bee colony algorithm radio transmitters distortion code division multiple access optimisation Multiple access communication wireless communication power amplifiers signal distortion optimal phase vector transmitter multicarrier code division multiple access system MC-CDMA signal computational complexity Radio links and equipment PAPR reduction ABC-PTS scheme communication complexity Amplifiers Other topics in statistics

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Probabilistic Dynamic Deployment of Wireless Sensor Networks by artificial bee colony algorithm

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SENSORS 2011年第6期11卷 6056-6065页

作者： Ozturk, Celal Karaboga, Dervis Gorkemli, Beyza Erciyes Univ Dept Comp Engn Fac Engn TR-38039 Kayseri Turkey

As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.

关键词： artificial bee colony algorithm wireless sensor networks dynamic deployment probabilistic detection model

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Distributed Path Planning of Swarm Mobile Robots 12

Distributed Path Planning of Swarm Mobile Robots

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12th Asian Control Conference (ASCC)

作者： Lee, Ya-Ting Zeng, Song-Fung Chiu, Chian-Song Natl Taichung Univ Sci & Technol Dept Beauty Sci Taichung Taiwan Chung Yuan Christian Univ Dept Elect Engn Chungli 32023 Taiwan

ISBN: (纸本)9784888983006

This paper is mainly to solve the path planning problem of swarm robots. If swarm robots work in the same environment at the same time, it is necessary to have appropriate planning methods to enable individuals in the group to complete their tasks. The solution is to use the artificial bee colony algorithm for the problem of robot path planning. Because the artificial bee colony algorithm has a high efficiency to solve the optimization problem, it tries to solve the problem of robot path planning in a distributed manner. The concept of organizing robots achieves efficient task of information processing and group working in the environment.

关键词： Swarm robots path planning artificial bee colony algorithm distributed planning

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An improved artificial bee colony algorithm for directing orbits of chaotic systems

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APPLIED MATHEMATICS AND COMPUTATION 2011年第7期218卷 3868-3879页

作者： Gao, Wei-feng Liu, San-yang Jiang, Fei Xidian Univ Dept Math Xian 710071 Shannxi Peoples R China

artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we propose an improved ABC (IABC) by using a modified search strategy to generate a new food source in order that the exploration and exploitation can be well balanced and satisfactory optimization performances can be achieved. In addition, to enhance the global convergence, when producing the initial population, both opposition-based learning method and chaotic maps are employed. In this paper, the proposed algorithm is applied to control and synchronization of discrete chaotic systems which can be formulated as both multimodal numerical optimization problems with high dimension. Numerical simulation and comparisons with some typical existing algorithms demonstrate the effectiveness and robustness of the proposed approach. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Chaotic systems Directing orbit Synchronization artificial bee colony algorithm

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