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11th International Conference on Internet and Distributed Computing Systems (IDCS)

作者： He, Lijun Li, Wenfeng Zhang, Yu Cao, Jingjing Wuhan Univ Technol Sch Logist Engn Wuhan Peoples R China

ISBN: (纸本)9783030027384;9783030027377

swarm intelligence algorithm (SIA) is an important artificial intelligence technology, which has been widely applied in various research fields. Recently, adopting various multi-objective SIAs (MOSIAs) to solve multi-objective flow shop scheduling problem (MOFSP) has attracted wide research attention. However, there are fewer review papers on the MOFSP. Many new MOSIAs have been proposed to solve MOFSP in the last decade. Therefore, in this study, MOSIAs of MOFSP over the past decade are briefly reviewed and analyzed. Based on the existing problems and new trend of Industry 4.0, several new promising future research directions are pointed out. These research directions are: (1) new hybrid MOSIA;(2) MOSIA with high computational efficiency;(3) MOSIA based on machine learning and big data;(4) multi-objective approach;(5) many-objective flowshop scheduling.

关键词： swarm intelligence algorithm Multi-objective flow shop scheduling Machine learning Big data Multi-objective approach

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Wolf Pack algorithm: An Overview 17th

Wolf Pack Algorithm: An Overview

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17th International Conference on Intelligent Robotics and Applications

作者： Xu, Wei Wang, Yueming Xu, Peng Qiu, Tianqi Yan, Tong Wang, Zhirui China North Vehicle Res Inst Beijing 100072 Peoples R China China North Artificial Intelligence & Innovat Res Beijing 100072 Peoples R China Collect Intelligence & Collaborat Lab Beijing 100072 Peoples R China

ISBN: (纸本)9789819607945;9789819607952

Heuristic algorithms have evolved rapidly, leading to numerous theoretical breakthroughs and wide applications in various fields. swarm intelligence algorithms, as an important branch of heuristic algorithms, have attracted significant attention from researchers. With the expansion of application scenarios and increasing problem complexity, swarm intelligence algorithms have faced considerable challenges in dealing with high-dimensional, multimodal complex function optimization. As a novel swarm intelligence algorithm, the Wolf Pack algorithm (WPA) has emerged as an efficient tool for addressing such issues due to its unique swarm characteristics and the mechanism that simulates wolf pack hunting behavior. A systematic review and summary of WPA from the perspectives of biological characteristics and algorithm principles are of paramount significance. Subsequently, a comparison between WPA and commonly used swarm intelligence algorithms is conducted. Then, the research progress of WPA is discussed in terms of both theory and application. Finally, inadequacies of WPA are explored, and the future prospects are presented.

关键词： Heuristics algorithm swarm intelligence algorithm Wolf Pack algorithm (WPA) Application field

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Comparison of swarm intelligence algorithms in atmospheric compensation for free space optical communication

Comparison of swarm intelligence algorithms in atmospheric c...

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Conferences of the Photoelectronic-Technology-Committee of the Chinese-Society-of-Astronautics

作者： Li, Zhaokun Cao, Jingtai Liu, Wei Feng, Jianfeng Zhao, Xiaohui Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China

ISBN: (纸本)9781628416527

We use conventional adaptive optical system to compensate atmospheric turbulence in free space optical (FSO) communication system under strong scintillation circumstances, undesired wave-front measurements based on Shark-Hartman sensor (SH). Since wavefront sensor-less adaptive optics is a feasible option, we propose several swarm intelligence algorithms to compensate the wavefront aberration from atmospheric interference in FSO and mainly discuss the algorithm principle, basic flows, and simulation result. The numerical simulation experiment and result analysis show that compared with SPGD algorithm, the proposed algorithms can effectively restrain wavefront aberration, and improve convergence rate of the algorithms and the coupling efficiency of receiver in large extent.

关键词： free space optical communication wavefront sensorless adaptive optics swarm intelligence algorithm coupling efficiency

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Performance Comparison and Application of swarm intelligence algorithms in Crowd Evacuation 2020

Performance Comparison and Application of Swarm Intelligence...

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4th International Conference on Management Engineering, Software Engineering and Service Sciences (ICMSS)

作者： Zhao, Rongyong Liu, Qiong Li, Cuiling Wang, Yan Dong, Daheng Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9781450376419

With the improvement of computer computing speed and the development of swarm intelligence algorithms, more and more swarm intelligence algorithms are used in the research of crowd evacuation. Firstly, some typical swarm intelligence algorithms are introduced. And their performances and applicable scenarios are compared and analyzed. Based on the analysis of various swarm intelligence algorithms, a new crowd evacuation model based on swarm intelligence algorithm is proposed in this paper. This model is based on dynamic grouping principle, driven by particle swarm optimization in microscopic perspective and artificial bee colony algorithm in macroscopic perspective. It fully combines the advantages of particle swarm optimization (PSO) to simulate the crowd more realistically with the advantages of artificial bee colony algorithm for global optimization. This proposed model also has better computational convergence, which can provide a professional basis for crowd evacuation.

关键词： swarm intelligence algorithm Crowd Evacuation Particle swarm Optimization Artificial Bee Colony

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A swarm intelligence graph-based pathfinding algorithm (SIGPA) for multi-objective route planning

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COMPUTERS & OPERATIONS RESEARCH 2021年 133卷 105358-105358页

作者： Ntakolia, Charis Iakovidis, Dimitris K. Univ Thessaly Sch Sci Dept Comp Sci & Biomed Informat Volos Greece

Personalized tourist route planning (TRP) and navigation are online or real-time applications whose mathematical modeling leads to complex optimization problems. These problems are usually formulated with mathematical programming and can be described as NP hard problems. Moreover, the state-of-the-art (SOA) path search algorithms do not perform efficiently in solving multi-objective optimization (MO) problems making them inappropriate for real-time processing. To address the above limitations and the need for online processing, a swarm intelligence graph-based pathfinding algorithm (SIGPA) for MO route planning was developed. SIGPA generates a population whose individuals move in a greedy approach based on A* algorithm to search the solution space from different directions. It can be used to find an optimal path for every graph-based problem under various objectives. To test SIGPA, a generic MOTRP formulation is proposed. A generic TRP formulation remains a challenge since it has not been studied thoroughly in the literature. To this end, a novel mixed binary quadratic programming model is proposed for generating personalized TRP based on multi-objective criteria and user preferences, supporting, also, electric vehicles or sensitive social groups in outdoor cultural environments. The model targets to optimize the route under various factors that the user can choose, such as travelled distance, smoothness of route without multiple deviations, safety and cultural interest. The proposed model was compared to five SOA models for addressing TRP problems in 120 various scenarios solved with CPLEX solver and SIGPA. SIGPA was also tested in real scenarios with A* algorithm. The results proved the effectiveness of our model in terms of optimality but also the efficiency of SIGPA in terms of computing time. The convergence and the fitness landscape analysis showed that SIGPA achieved quality solutions with stable convergence.

关键词： Mixed integer non-linear programming swarm intelligence algorithm Path search algorithm Convergence analysis Tourist route planning

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Research on optimal control of HVAC system using swarm intelligence algorithms

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BUILDING AND ENVIRONMENT 2023年第1期241卷

作者： Miao, Yurun Yao, Ye Hong, Xiaoxi Xiong, Lei Zhang, Fuqing Chen, Wanting Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen Sch Mech Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

Energy conservation in buildings is of vital importance for solving the problem of global warming and energy scarcity in today's world. In this paper, we discuss the optimal control methods of the heating, ventilation, and air-conditioning (HVAC) system using swarm intelligence algorithms to reduce energy consumption of buildings. The performances of ten optimization algorithms are compared. The results on test functions manifest the artificial bee colony algorithm (ABC) exhibits the best performance among the focused optimization algorithms. Taking a real HVAC system as an example, the holistic energy models of equipment in the HVAC system are established and validated. Then, the focused algorithms are used for the optimal control of a real HVAC system aiming at energy conservation. The simulated results based on 24-h real operation data show that the energy -saving ratio through by the ABC can reach 24.07% on average. The work in this study will contribute to the building energy conservation and carbon neutral development in the future.

关键词： HVAC system swarm intelligence algorithm Energy conversation Optimal control

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Robust mixed source localization in WSN using swarm intelligence algorithms

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DIGITAL SIGNAL PROCESSING 2020年第0期98卷 102651-000页

作者： Maruthi, Shree Prasad Panigrahi, Trilochan NIT Goa Dept Elect & Commun Engn Ponda 403401 Goa India

Passive localization and classification algorithms for mixed near-field and far-field sources have mainly been investigated for antenna arrays with regular or symmetrical geometry. However, these algorithms may not be applicable to wireless sensor networks, where spatially distributed sensor nodes form an array of random geometry. This paper proposes squared error norm of residual fitting error matrix (SRFEM) cost function for mixed source localization, which does not place any constraint on the geometry of antenna array. The proposed cost function is optimized using computationally simple swarm intelligence (SI) algorithms. Convergence performance of three SI algorithms, i.e., particle swarm optimization, Whale optimization, and Grey Wolf optimization is investigated to obtain estimates of the source location parameters with a minimal number of iterations or computational complexity. In order to provide robustness for the SRFEM cost function, this paper proposes an impulse denoising technique based on the long-short term memory recurrent neural network (LSTM-RNN). Simulation results confirm that, in the presence of AWGN, the proposed SRFEM cost function outperforms existing techniques based on the uniform linear array and approaches the theoretical Cramer-Rao lower bound even for a fewer number of snapshots. Further, simulation analysis also reveals that LSTM-RNN provides better mixed localization performance under impulse noise as compared to the bounded noise covariance approach at high signal-to-noise values. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Mixed source localization Impulse noise swarm intelligence algorithm LSTM-RNN Bounded non-linear covariance function

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Differential privacy may have a potential optimization effect on some swarm intelligence algorithms besides privacy-preserving

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INFORMATION SCIENCES 2024年 654卷

作者： Zhang, Zhiqiang Zhu, Hong Xie, Meiyi Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

Differential privacy (DP), as a promising privacy-preserving model, has attracted great interest from researchers in recent years. At present, research on the combination of deep learning and DP is active. In contrast, another widely used artificial intelligence technique, swarm intelligence (SI), has received little attention in the context of DP even though it also triggers privacy concerns. For this reason, this paper attempts to combine DP and SI for the first time and proposes a general differentially private swarm intelligence algorithm framework (DPSIAF). By utilizing the exponential mechanism, this framework can easily develop existing SI algorithms into the private versions. As examples, we apply it to four popular SI algorithms, and the related analyses demonstrate the effectiveness of our DPSIAF. More importantly, our private algorithms also exhibit some interesting experimental phenomena. In some cases, their performance is not strictly affected by the privacy budget, and one of them even outperforms its non-private version. These findings are different from the conventional cognition, which indicates DP owns the potential to serve as an optimization tool for some SI algorithms. Our study may provide a new perspective on DP and promote the synergy between metaheuristic optimization community and privacy computing community.

关键词： Differential privacy Metaheuristic optimization Privacy protection swarm intelligence algorithm

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A Novel Deployment Strategy Based on Improved Gradient-Based Optimizer for BLE Anchor Nodes

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TRANSACTIONS IN GIS 2025年第1期29卷

作者： Yan, Jinjin Zhang, Manyu Gu, Fuqiang Li, You Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Ctr Qingdao Peoples R China Chongqing Univ Coll Comp Sci Chongqing Peoples R China Wuhan Univ State Key Lab Surveying Mapping & Remote Sensing Wuhan Peoples R China

Bluetooth low energy (BLE) technology has gained attraction for indoor localization in recent years due to its low power consumption and cost-effectiveness. However, deploying BLE anchor nodes in indoor environments, particularly in areas with obstacles that can interfere with signal strength, remains a major challenge. This paper proposes a novel deployment strategy that utilizes an improved gradient-based optimizer (GBO) algorithm to address these challenges. The primary objective is to improve both the coverage of BLE anchor nodes and the accuracy of localization. To assess the effectiveness of the deployment schemes generated by the algorithm, we introduce a comprehensive performance evaluation metric. This metric integrates multiple indicators, including capability rate, geometric accuracy factor, penalty term, sensor network topology, and network uniformity. An obstacle perception model is also introduced to mitigate the impact of deviations between measured values from the RSSI ranging model in obstacle-rich environments and actual ground truth values. This model adjusts the measurements to enhance the reliability of the localization process. The presented algorithm is tested in an indoor space with obstacles. Experimental results demonstrate that the novel algorithm significantly optimizes anchor node layouts, enhancing coverage, and localization accuracy. Compared with the existing algorithms, the proposed method shows better performances and highlights its potential for more accurate and efficient BLE indoor localization. In particular, our research achieved an 86% probability of positioning errors within 3 m, surpassing the best results of other deployment schemes by approximately 11%. This highlights the potential of our approach for more accurate and efficient BLE-based indoor localization.

关键词： anchor node deployment Bluetooth low energy indoor positioning swarm intelligence algorithm

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Odor source localization of multi-robots with swarm intelligence algorithms: A review

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 949888页

作者： Wang, Junhan Lin, Yuezhang Liu, Ruirui Fu, Jun Zhejiang Gongshang Univ Sch Comp Sci & Informat Engn Artificial Intelligence Things & Robot Lab Hangzhou Peoples R China

The use of robot swarms for odor source localization (OSL) can better adapt to the reality of unstable turbulence and find chemical contamination or hazard sources faster. Inspired by the collective behavior in nature, swarm intelligence (SI) is recognized as an appropriate algorithm framework for multi-robot system due to its parallelism, scalability and robustness. Applications of SI-based multi-robots for OSL problems have attracted great interest over the last two decades. In this review, we firstly summarize the trending issues in general robot OSL field through comparing some basic counterpart concepts, and then provide a detailed survey of various representative SI algorithms in multi-robot system for odor source localization. The research field originates from the first introduction of the standard particle swarm optimization (PSO) and flourishes in applying ever-increasing quantity of its variants as modified PSOs and hybrid PSOs. Moreover, other nature-inspired SI algorithms have also demonstrated the diversity and exploration of this field. The computer simulations and real-world applications reported in the literatures show that those algorithms could well solve the main problems of odor source localization but still retain the potential for further development. Lastly, we provide an outlook on possible future research directions.

关键词： odor source localization swarm intelligence algorithm multi-robot system particle swarm optimization mobile robot nature-inspired computation

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