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When computation Hugs intelligence: Content-Aware Data Processing for Industrial IoT

IEEE INTERNET OF THINGS JOURNAL 2018年第3期5卷 1657-1666页

作者： Zhou, Liang Wu, Dan Chen, Jianxin Dong, Zhenjiang Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Network Technol Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Commun & Informat Engn Nanjing 210003 Jiangsu Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210003 Jiangsu Peoples R China Army Engn Univ PLA Inst Commun Engn Nanjing 210007 Jiangsu Peoples R China ZTE Corp Cloud & IT Inst Nanjing 210012 Jiangsu Peoples R China

Data service has been considered as one the most prominent characteristics for Industrial Internet of Things (IIoT). This paper studies how to design an optimal computing manner for a general IIoT system. On the theory end, we analyze the relationship between the data processing and the energy consumption through investigating the content correlation of the captured data. Importantly, we derive an exact expression for the performance of IIoT by combining computation with intelligence. On the application end, we design an efficient way to obtain a threshold by approximating the performances of different computing manners, and show how to apply it to practical IIoT applications. We believe that the proposed computation rules hold great significance for the IIoT designer, that is, it is better to use distributed computing manner when the content correlation is high, otherwise, centralized computing manner is better.

关键词： computation intelligence computing manner data processing Industrial Internet of Things (IIoT)

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Study on Intelligent Analysis and Processing Technology of Computer Big Data Based on Clustering Algorithm

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2023年第2期16卷 150-158页

作者： Liu, Xiaoming Rokunojjaman, Md Kumar, Rakesh E. R. Nazila, Ragimova Vugar, Abdullayev JingZhou Vocat Coll Technol Software Engn Inst Jingzhou 434000 Hubei Peoples R China Chongqing Univ Technol Dept Comp Sci & Technol 69 Hongguang Ave Chongqing 400054 Peoples R China Texila Amer Univ East Bank Demerara Guyana Azerbaijan State Oil & Ind Univ Baku Azerbaijan

Aim: Clustering belongs to unsupervised learning, which divides the data objects into the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity. Background: The clustering of spatial data objects can be solved by optimization based on the clustering objective function. Objective: Study on intelligent analysis and processing technology of computer big data based on clustering algorithm. Methods: First, a new dynamic self-organizing feature mapping model is proposed, and the training algorithm of the model is given. Then, the spectral clustering technology and related concepts are introduced. The spectral clustering algorithm is studied and analyzed, and a spectral clustering algorithm that automatically determines the number of clusters is proposed. Furthermore, an algorithm for constructing a discrete Morse function to find the optimal solution is proposed, proving that the constructed function is the optimal discrete Morse function. At the same time, two optimization models based on the discrete Morse theory are constructed. Finally, the optimization model based on discrete Morse theory is applied to cluster analysis, and a density clustering algorithm based on the discrete Morse optimization model is proposed. Results: This study is focused on designing and implementing a partitional-based clustering algorithm based on big data, that is suitable for clustering huge datasets to meet low computational requirements. The experiments are conducted in terms of time and space complexity and it is observed that the measure of clustering quality and the run time is capable of running in very less time without negotiating the quality of clustering. The results show that the experiments are carried out on the artificial data set and the UCI data set. Conclusion: The efficiency and superiority of the new model, are verified by comparing it with the clustering results of the DBSCAN algorithm.

关键词： computation intelligence cluster analysis genetic algorithm big data clustering algorithm intelligent analysis

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A Path Planning Approach for Optic Inspection Based on Composite Frame 6

A Path Planning Approach for Optic Inspection Based on Compo...

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6th International Conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Zhang, Yukang Zheng, Jianchao Luo, Junwei Yan, Xiaohui Dongguan Univ Technol Sch Mech Engn Dongguan Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Path planning for Automatic Optical Inspection (AOI) is a key strategy for enhancing the efficiency of AOI devices. This paper modeled the inspection issue upon the completion of imaging window planning and introduced a composite frame to optimize the problem. Swarm intelligence algorithms were employed to determine the position of imaging points, while the 2-opt algorithm was utilized to optimize the access order. The edge location method was proposed to reduce the solution space. To address the slow convergence and low accuracy issues for BSO algorithm, an improved version was proposed. The strategy of individual discussion was improved, the individual step length was adjusted dynamically according to the population position, and the competitive crossover strategy was introduced. Experimental results confirm the effectiveness of the improved brain storm algorithm and the composite frame.

关键词： computation intelligence Brain storm optimization Algorithm design Path planning Automatic optical inspection

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Differential Vectors Empower Snow Ablation Optimizer 11

Differential Vectors Empower Snow Ablation Optimizer

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11th Joint International Information Technology and Artificial intelligence Conference, ITAIC 2023

作者： Lu, Tianfang Li, Haotian Yang, Yifei Yu, Zhiwei Lei, Zhenyu Gao, Shangce University of Toyama Faculty of Engineering Toyama930-8555 Japan

ISBN: (纸本)9798350333664

The Snow Ablation Optimizer (SAO) algorithm represents a new intelligent search method founded on the sublimation and melting phenomena of snow. It incorporates a dual-population mechanism to balance exploration and exploitation. However, one limitation of SAO is its propensity to become trapped in local optima. Coincidentally, the Differential Evolution (DE) algorithm exhibits strong global search capabilities. In light of this, we propose a fusion algorithm, named DESAO, that combines the strengths of SAO and DE. Our experimental results, conducted on the IEEE CEC2017 benchmark, demonstrate DESAO's superiority of optimization capabilities and rapid convergence rates. © 2023 IEEE.

关键词： computation intelligence Differential Evolution Meta-heuristic Algorithms Snow Ablation Optimizer

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Serial multilevel-learned differential evolution with adaptive guidance of exploration and exploitation

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期255卷

作者： Yu, Jiatianyi Wang, Kaiyu Lei, Zhenyu Cheng, Jiujun Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China

Recent years have witnessed a surge in the development of multilevel variants of differential evolution (DE), significantly enhancing the performance of DE algorithms. However, systematically guiding algorithms to strike a balance between exploration and exploitation within a parallel multilevel structure remains a challenge. In response to this challenge, we propose serial multilevel-learned differential evolution (SMLDE) with adaptive guidance for exploration and exploitation. This algorithm establishes a tightly connected multilevel-learned structure and an adaptive current best level. It also incorporates a combination of strategies including single iterative adaption, Cauchy perturbation, and iterative constraint strategy into each of the adapted levels, thus enhancing inter-component connections and dynamically balancing exploration and exploitation. To validate its effectiveness, we conduct ablation experiments and visualized analyses of exploration and exploitation to demonstrate the reliable strength of the multilevel-learned structure. The experimental results comparing SMLDE with 15 state-of-the-art algorithms using the IEEE Conference on Evolutionary computation (CEC) 2017 benchmark test sets across various dimensions showcase its superior performance. Additionally, its remarkable results on the CEC2011 benchmark test and two real-world engineering optimization problems underscore the robustness and effectiveness of SMLDE.

关键词： Differential evolution Multilevel-learned Exploration and exploitation computation intelligence Population structure

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Self-adaptive salp swarm algorithm for engineering optimization problems

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APPLIED MATHEMATICAL MODELLING 2021年第Part1期89卷 188-207页

作者： Salgotra, Rohit Singh, Urvinder Singh, Supreet Singh, Gurdeep Mittal, Nitin Thapar Inst Engn & Technol Dept ECE Patiala Punjab India Chandigarh Univ Dept ECE Mohali India

Salp swarm algorithm is a recent introduction in the field of swarm intelligent algorithms and has proved its worth over various research domains. Though it is a competitive algorithm but it has been found that salp swarm algorithm suffers from various problems including poor exploitation, slow convergence and unbalanced exploration and exploitation operation. In present work, four major modifications have been added to salp swarm algorithm in order to make it self-adaptive and the proposed algorithm has been named as adaptive salp swarm algorithm. The modifications include division of generations and logarithmic adaptive parameters to control the extent of exploration and exploitation, enhanced exploitation phase to improve the local search and linearly decreasing population adaptation to reduce the total number of function evaluations. The performance of the proposed algorithm is tested on benchmark problems and further applied for optimization of transmission parameters in cognitive radio system. From the experimental results, it has been found that the proposed adaptive salp swarm algorithm is highly competitive and provides better results when compared with bat algorithm, grey wolf optimization, teacher learning based algorithm, dragonfly algorithm and others. Convergence profiles and statistical tests further validate the results. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Optimization Salp swarm algorithm Self-adaptive parameters computation intelligence Cognitive radio system

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Multiple Solutions Based Particle Swarm Optimization for Cluster-Head-Selection in Wireless-Sensor-Network

Multiple Solutions Based Particle Swarm Optimization for Clu...

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IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2)

作者： Jan, Sakin Masood, Mohsin Abasyn Univ Dept Comp & Technol Peshawar Pakistan

ISBN: (纸本)9781665412858

Wireless sensor network (WSN) has a significant role in wide range of scientific and industrial applications. In WSN, within the operation area of sensor nodes the nodes are randomly deployed. The constraint related to energy is considered as one of the major challenges for WSN, which may not only affect the sensor nodes efficiency but also influences the operational capabilities of the network. Therefore, numerous attempts of researches have been proposed to counter this energy problem in WSN. Hierarchical clustering approaches are popular techniques that offered the efficient consumption of the energy in WSN. In addition to this, it is understood that the optimum choice of sensor as cluster head can critically help to reduce the energy consumption of the sensor node. In recent years, metaheuristic optimization is used as a proposed technique for the optimal selection of cluster heads. Furthermore, it is noteworthy here that proposed techniques should be efficient enough to provide the optimal solution for the given problem. Therefore, in this regard, various attempts are made in the form of modified versions or new metaheuristic algorithms for optimization problems. The research in the paper offered a modified version of particle-swarm-optimization (PSO) for the optimal selection of sensor nodes as cluster heads. The performance of the suggested algorithm is experimented and compared with the renowned optimization techniques. The proposed approach produced better results in the form of residual energy, number of live nodes, sum of dead nodes, and convergence rate.

关键词： artificial intelligence computation intelligence metaheuristics optimization wireless-sensor-networks

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A proportional, integral and derivative differential evolution algorithm for global optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 206卷

作者： Jiang, Ruiye Shankaran, Rajan Wang, Songyan Chao, Tao Harbin Inst Technol Control & Simulat Ctr Harbin 150001 Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia

The proportional, integral, and derivative differential evolution algorithm (PID-DE) is proposed as a new type of interdisciplinary metaheuristic evolutionary algorithm in this paper. The inspiration of PID-DE is derived from the classical proportional, integral, and derivative control method in engineering, and it is used in the framework of the differential evolution (DE) algorithm. To begin, the mathematical models of proportional search, integral search, and derivative search are established as the fundamental search operations. Five different types of optimizers that use a combination of these three basic operations and an additional mutation operation are presented. The selection and crossover methods in DE are then modified to maintain population diversity while also improving global search capacity, and a feedback strategy is established to adaptively adjust the subgroup member of each optimizer. Following that, an integrated high-accuracy, rapid-convergence, and stable metaheuristic is invented using the comprehensive information utilization principle and flexible parameter determination method. Five groups of experiments are studied to assess the overall performance of the proposed algorithm. The first test comprises 12 standard benchmark functions with minimum optima. In Tests 2 and 3, the 52 functions of Congress on Evolutionary computation (CEC) 2014 and CEC 11 are evaluated using PID-DE under standard test conditions. Besides, three classic real-world engineering design problems and the CEC 2020 test suit are studied for the constrained optimization test. The experimental tests validate PID-DE's higher accuracy and faster convergence speed in numerical optimization when compared with the representative approaches and top algorithms in the CEC competitions.

关键词： CEC optimization functions computation intelligence Constrained engineering problem Evolutionary algorithm PID controller New metaheuristic

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An improved whale optimization algorithm with armed force program and strategic adjustment

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APPLIED MATHEMATICAL MODELLING 2020年第0期81卷 603-623页

作者： Jiang, Ruiye Yang, Ming Wang, Songyan Chao, Tao Harbin Inst Technol Control & Simulat Syst Harbin 150001 Heilongjiang Peoples R China

In this paper, we propose a new whale army optimization algorithm with a view to solving multifarious optimization problems. The key novelty of our approach is to modify the original whale optimization algorithm to make it effective to solve the complicated, large-scale and constrained optimization problems. Our modifications mainly embody two aspects: the beneficial strategic adjustment to set key parameters and to help establish base principles in the original optimizer and the introduction of armed force program which classifies the search whales into different categories to achieve efficient cooperation. We evaluate the performance of the proposed algorithm, using three simple benchmark test functions over thirty CEC-2014 real-parameter numerical optimization problems and three constraint engineering design problems. The test results indicate that this algorithm can provide a faster local convergence rate, a higher convergence accuracy, and a lower computational complexity in comparison to traditional whale optimization algorithms and other sophisticated state of the art whale optimizers. Performance wise, it also surpasses many advanced methods for large-scaled complex functions. Furthermore, in this paper we propose a variant of whale army optimization algorithm to specifically address and solve optimizing constrained problems with a high degree of precision. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Constrained functions computation intelligence CEC optimization functions Engineering design problems Swarm intelligence Whale optimization algorithm

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Recent Meta-Heuristics Improved by Self-Adaptation Applied to Nonlinear Model-Based Predictive Control

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IEEE ACCESS 2020年 8卷 118841-118852页

作者： Mesquita, Eduardo De Mendonca Sampaio, Renato Coral Hultmann Ayala, Helon Vicente Llanos, Carlos Humberto Univ Fed Goias Sch Elect & Comp Engn BR-74605010 Goiania Go Brazil Univ Brasilia Fac Gama BR-72444240 Brasilia DF Brazil Univ Brasilia Fac Technol BR-70 91090 Brasilia DF Brazil Pontifical Catholic Univ Rio De Janeiro Dept Mech Engn BR-22451900 Rio De Janeiro Brazil

Nonlinear Model-based Predictive Control (NMPC) is a relevant research area having applications in the industrial sector. Traditionally, in this technique, gradient descent algorithms have been used to solve the related optimization problem. More recently, bio-inspired meta-heuristics have also been applied to this problem. However, only a few works have been devoted to testing solvers that use parameter control with self-adaptive traits, which allows mitigating the problem of offline parameter tuning in bio-inspired approaches. In this paper, we propose the novel Adaptive Modified Grey Wolf Optimization (AMGWO) and the Adaptive Moth-Flame Optimization (AMFO), for solving Nonlinear Model-based Predictive Control (NMPC) problems. To achieve this, a mechanism for individual leaders weighting and a crossover operator are introduced in AMGWO, and a simple self-adaptive parameter technique is applied in both meta-heuristics. The improved solvers are tested to perform the swing-up of a single inverted pendulum and attitude control of a satellite, which are nonlinear problems relevant for assessing control performance. Nonparametric statistical tests are applied to compare the improved meta-heuristics optimization outcomes with other five meta-heuristics, which shows that the self-adaptive parameter technique can significantly improve the performance when applied as an NMPC solver, as the AMFO and AMGWO statistically outperform or performs as well as all algorithms compared in both the pendulum and satellite control, respectively. This is important as improving the optimizer efficiency will lead to more accurate control and enable rapid hardware implementation.

关键词： Adaptive algorithms computation intelligence nonlinear dynamical systems predictive control

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