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A Hybrid Path Planning and Formation Control Strategy of Multi-Robots in a Dynamic Environment

JOURNAL OF ADVANCED COMPUTATIONAL intelligence AND INTELLIGENT INFORMATICS 2022年第3期26卷 342-354页

作者： Zhou, Meng Wang, Zihao Wang, Jing Dong, Zhe North China Univ Technol Sch Elect & Control Engn 5 Jinyuanzhuang Rd Beijing 100144 Peoples R China

This paper proposes a hybrid path planning and formation control strategy for multi-robots in a dynamic environment. Under a leader-follower formation structure, the followers can track the motion of one leader after the leader's path is determined. First, a hybrid path planning strategy that contains global path planning and local path planning of the leader is investigated, in which an improved hybrid grey wolf optimizer with whale optimizer algorithm (GWO-WOA) is designed for the global path planning in a given map, meanwhile, a dynamic window approach (DWA) is fused for the local path planning to avoid dynamic obstacles. Then, a leader-follower formation control algorithm is proposed for multiple mobile robots. The followers are controlled to track their corresponding virtual robots which are generated according to the leader's position and the formation. Finally, simulation experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithm in different environments.

关键词： multi-robots formation control hybrid motion planning improved GWO-WOA algorithm swarm intelligence algorithm controller design

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Combine Discussion Mechanism and Chaos Strategy on Particle swarm Optimization algorithm 10

Combine Discussion Mechanism and Chaos Strategy on Particle ...

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10th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Fan, Xinwen Ye, Yin Chen, Zhenyi Hong, Zhixing Qiu, Zhenyu Dong, Chen Fuzhou Univ Sch Math & Comp Sci Fuzhou Peoples R China Univ S Florida Dept Elect Engn Tampa FL 33620 USA

ISBN: (纸本)9781728109459

Particle swarm optimization (PSO) is one of the artificial intelligence algorithms and has been proved to be an effective global optimization method. In this paper, a novel particle swarm optimization algorithm (DC-PSO) combining the discussion mechanism and chaos strategy is proposed to improve the standard PSO (SPSO) which is easy to trap into local optimum and has the problems of premature convergence. The four main strategies adapted in DC-PSO are chaos initializes the population, dynamic change of population, discussion mechanism of double populations and random variation of particles. Experiments are performed on eight benchmark functions to test the accuracy and performance of DC-PSO. Experiments show the proposed DC-PSO performs better than DEs intelligent algorithms.

关键词： Particle swarm optimization Discussion mechanism Chaos strategy swarm intelligence algorithm

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Ancient Migration algorithm: A Behavioral Path Model Study of the Lowest-cost Goal from an Anthropological Perspective

Ancient Migration Algorithm: A Behavioral Path Model Study o...

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IEEE/WIC/ACM International Joint Conference on Web intelligence and Intelligent Agent Technology (WI-IAT)

作者： Weng, Ziyang Hu, Yidan Li, Hui Tang, Xiaoyu Weng, Zhimo Wuhan Univ Sch Informat Management Wuhan Peoples R China Altay City Culture Sports Radiot Televis & Touris Altay Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Zhongnan Univ Econ & Law China Korea Inst New Media Wuhan Peoples R China

ISBN: (纸本)9781665494021

Based on the construction of the geographic data elevation model in the GIS environment, combined with the long-term paleoclimate data, this work realizes mountain snow accumulation, water level cutting and restoration, vegetation ecological simulation, restores the paleo-ecological geological structure, and restricts the migration algorithm. It is extended to the behavioral deduction of ancient human migration, and the path models with the lowest cost of human migration in harsh natural environments are established taking into account the geological diversity, namely the Forest-Water Model and the Snow Line Model. Through the deduction, the effective support of the original Altay population for the route to the north was obtained, the northward migration of the ethnic branch was completed by crossing the snow-capped mountain area.

关键词： swarm intelligence algorithm routes of ancient human activity ancient geographical environment Altay Region

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Differential Evolution Schemes for Speech Segmentation: A Comparative Study

Differential Evolution Schemes for Speech Segmentation: A Co...

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2014 IEEE Symposium on Differential Evolution (SDE)

作者： Iliya, Sunday Neri, Ferrante Menzies, Dylan Cornelius, Pip Picinali, Lorenzo De Montfort Univ Sch Comp Sci & Informat Ctr Computat Intelligence Leicester LE1 9BH Leics England De Montfort Univ Sch Engn Media & Sustainable Dev Ctr Elect & Commun Engn Leicester LE1 9BH Leics England De Montfort Univ Sch Allied Hlth Sci Human Commun Studies Leicester LE1 9BH Leics England De Montfort Univ Sch Engn Media & Sustainable Dev Interact & Media Technol Leicester LE1 9BH Leics England

ISBN: (纸本)9781479944613

This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback. The functioning of the signal processing technique has been optimized by selecting the parameters of the model. The optimization has been carried out by testing and comparing multiple Differential Evolution implementations, including a standard one, a memetic one, and a controlled randomized one. Numerical results have also been compared with a famous and efficient swarm intelligence algorithm. For the given problem, Differential Evolution schemes appear to display a very good performance as they can quickly reach a high quality solution. The binomial crossover appears, for the given problem, beneficial with respect to the exponential one. The controlled randomization appears to be the best choice in this case. The overall optimized system proved to segment well the speech utterances and efficiently detect its uninteresting parts.

关键词： evolutionary computation speech processing swarm intelligence binomial crossover differential evolution disordered utterances musculoskeletal articulation data short speech utterances signal processing speech segmentation swarm intelligence algorithm Correlation Delays Educational institutions Feature extraction Optimization Speech Steady-state Particle swarm optimization signal processing Speech Speech processing Educational institutions evolutionary algorithm Feature extraction Steady state delay

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Improving machine learning accuracy in diagnosing diseases using feature selection based on the fruit- fly algorithm 6

Improving machine learning accuracy in diagnosing diseases u...

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6th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Haghighi, Mehdi Salkhordeh Hoseini, Mohammad Javad Mashhadi Sadjad Univ Technol Fac Comp Engn & IT Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781728186290

Data mining methods can be used to classify people as sick or healthy and to diagnose diseases. One of the important challenges in diagnosing the disease using learning methods such as multilayer artificial neural network is the optimal selection of its parameters that can be solved to some extent with meta-heuristic methods, but these techniques are not without problems because in these methods meta-heuristic algorithm To a certain extent, it can only reduce the error, and to reduce the machine learning error, in addition to the optimal selection of learning parameters, it is necessary to consider and select the appropriate features. The proposed method has two layers and in the first layer the selection of weight and bias of the multilayer neural network is done using the fruit fly algorithm and in the input layer, the feature selection phase is done using the binary version of the fruit fly optimization algorithm to learn to Do it only on the important features and reduce the problem space and increase the learning speed and accuracy. The results of tests on several disease datasets in the UCI database show that the accuracy, sensitivity, and diagnosis of the proposed method are 98.36%, 98.12%, and 98.08%, respectively. The proposed method is more accurate in diagnosing diabetes than the PSO, FA, SHO, and HHO algorithms.

关键词： Feature selection data mining fruit fly algorithm machine learning Disease diagnosis swarm intelligence algorithm

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Discrete Wolf Pack Search algorithm Based Transit Network Design 7

Discrete Wolf Pack Search Algorithm Based Transit Network De...

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7th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Wu, Ruirui Wang, Shengsheng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781467399043

Transit network design problem is a typical NP-complete problem, when designing the transit network, we have to maximize the number of satisfied passengers, the total number of transfers and the total travel time of all served passengers. To some extent, it is also a multi-criteria decision-making problem. In this paper, a new model based on discrete wolf pack search algorithm (DWPS) is proposed to solve this problem. Our method can coordinate the exploration and exploitation abilities, which are the main factors that affect the performance of swarm intelligence algorithms. The experimental results show that the proposed DWPS is quite competitive compared with many existing techniques.

关键词： transit network design problem NP-complete problem multi-criteria decision-making problem swarm intelligence algorithm discrete wolf pack search algorithm

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The Design of Two-fold Redundancy Linear Arrays in Aperture Synthesis Radiometers

The Design of Two-fold Redundancy Linear Arrays in Aperture ...

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International Symposium of IEEE-Antennas-and-Propagation-Society / USNC/URSI National Radio Science Meeting

作者： Tang, Peiwen Lang, Liang Hu, Fei Zhu, Dong Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781538632840

In order to maintain the reliability of the interferometric aperture synthesis radiometer systems, especially in the situation of antenna/receiver failure, two-fold redundancy linear arrays (TFRLAs) are proposed in this paper. The optimization model of TFRLAs is formulated to achieve the highest resolution in the sense of two-fold redundancy that ensures the coverage of given spatial frequency (or baseline) domain even when any one of the antenna elements is broken. And then we introduce Fireworks algorithm (FWA) to solve this array design problem. Moreover, the performance of FWA, in terms of both optimization effect and computational cost, is evaluated by comparing that of a widely used swarm intelligence algorithm, named Particle swarm Optimization (PSO). Numerical results are provided with respect to the number of antennas from 13 to 29 and shown that the fesibility and effectiveness of the presented method.

关键词： Interferometric aperture synthesis radiometer (IASR) swarm intelligence algorithm system reliability two-fold redundancy linear arrays (TFRLAs)

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Processing parameters optimization based on PSO algorithm

Processing parameters optimization based on PSO algorithm

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2nd International Conference on Functional Manufacturing and Mechanical Dynamics

作者： Wu, Rongzong Liu, Qingjian Shao, Mingkun Wang, Run Tianjin Univ Sch Mech Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9783037852989

Particle swarm optimization (PSO) is classified into swarm intelligence algorithm. Focusing on the NC processing parameters optimization, this paper has made several improvements on PSO: dividing solutions into feasible and infeasible, then shrinking the infeasible solutions;dividing feasible solutions into inferior and non-inferior, then choosing the non-inferior solution with most domination solutions as the global best position for inferior solutions. It has shown a significant achievement in actual processing.

关键词： Processing parameters optimization PSO swarm intelligence algorithm

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Path Planning of Mobile Robot Based on Improved Wolf swarm algorithms 8

Path Planning of Mobile Robot Based on Improved Wolf Swarm A...

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IEEE 8th Joint International Information Technology and Artificial intelligence Conference (ITAIC)

作者： Chen, Xu Zhang, Yi Li, Kui Huang, Baiyue Chongqing Univ Posts & Telecommun Chongqing Peoples R China Key Lab Intelligent Mfg & Robot Chongqing Peoples R China

ISBN: (纸本)9781538681787

Aiming at solving the shortcomings of traditional wolf swarm algorithm, such as low search efficiency due to fixed search direction and radius, and easy to fall into local optimum when updating rules, an improved wolf swarm algorithm is proposed in this paper. Firstly, the exploding rules of fireworks algorithm are introduced to reduce the exploring steps of wolves near prey and increase the searching direction in order to improve the ability of local exploration, while the wolves far from prey the searching radius are increased and the searching direction are decreased in order to improve the global search ability. Secondly, the moving step size of running behavior is improved automatically to adjust the step size according to the location of each wolf which can improve the efficiency of running behavior. Finally, the updating rules of wolf swarm algorithm is improved for the individual wolf swarm is selected according to probability to enhance the global optimization ability of the algorithm. The performance test in test function and path planning simulation experiment results show that the improved wolf swarm algorithm has faster convergence speed and higher convergence accuracy.

关键词： swarm intelligence algorithm wolf swarm algorithm path planning adaptive step size

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A Novel Bat algorithm based on Collaborative and Dynamic Learning of Opposite Population 22

A Novel Bat Algorithm based on Collaborative and Dynamic Lea...

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22nd IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Yong, Jiashi He, Fazhi Li, Haoran Zhou, Weiqing Wuhan Univ State Key Lab Software Engn Wuhan Hubei Peoples R China Wuhan Univ Sch Comp Sci & Technolgy Wuhan Hubei Peoples R China Sch Comp Sci & Technolgy Wuhan Hubei Peoples R China

ISBN: (纸本)9781538614822

As a new kind of swarm intelligence algorithms, bat algorithm is inspired by the bat's echolocation model to search an optimization solution. In this paper, we propose a novel bat algorithm based on collaborative and dynamic learning of opposite population. The proposed algorithm adapts a collaborative strategy to generate the opposite population. Therefore more possible opposite individuals can be dynamically learned and added to the population. We also present elite choices for the current and the opposite population. In this way, the search diversity and search intensity can be achieved. The experimental results of 8 typical test functions show that the proposed algorithm has the characteristics of fast convergence and avoiding falling into local optimal solution.

关键词： Bat algorithm Opposition-based Learning Function optimization swarm intelligence algorithm

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