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A fast SAR image segmentation method based on improved chicken swarm optimization algorithm

MULTIMEDIA TOOLS AND APPLICATIONS 2018年第24期77卷 31787-31805页

作者： Liang, Jianhui Wang, Lifang Ma, Miao Zhang, Jian Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China HaiNan Univ Inst Trop Agr & Forestry Dan Zhou 571737 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Shaanxi Peoples R China

Severe speckle noise existed in synthetic aperture radar (SAR) image presents a challenge to image segmentation. Though some traditional segmentation methods for SAR image have some success, most of them fail to consider segmentation effects and segmentation speed at the same time. In this paper, we propose a novel method of SAR image fast segmentation which is based on an improved chicken swarm optimization algorithm. In this method, the positions of the whole chicken swarm are firstly initialized in a narrowed foraging space. Secondly, the grey entropy model is selected as the fitness function of the improved chicken swarm optimization algorithm. Hence, the optimal threshold value is located gradually and quickly by virtue of the foraging behaviors of chicken swarm with a hierarchal order. Experimental results show that our method is superior to some segmentation methods based on genetic algorithm, artificial fish swarm algorithm in convergence, stability and segmentation effects.

关键词： Image segmentation chicken swarm optimization algorithm swarm intelligence SAR image

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Recent Studies on chicken swarm optimization algorithm: a review (2014-2018)

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第3期53卷 1737-1765页

作者： Deb, Sanchari Gao, Xiao-Zhi Tammi, Kari Kalita, Karuna Mahanta, Pinakeswar Indian Inst Technol Ctr Energy Gauhati India Univ Eastern Finland Sch Comp Kuopio Finland Aalto Univ Dept Mech Engn Espoo Finland Indian Inst Technol Dept Mech Engn Gauhati India

Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. chicken swarm optimization algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.

关键词： chicken swarm optimization algorithm Nature inspired intelligence optimization algorithm Applications Review

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An Improved chicken swarm optimization algorithm and its Application in Robot Path Planning

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IEEE ACCESS 2020年 8卷 49543-49550页

作者： Liang, Ximing Kou, Dechang Wen, Long Beijing Univ Civil Engn & Architecture Sch Sci Beijing 102600 Peoples R China Guizhou Univ Finance & Econ Key Lab Econ Syst Simulat Guiyang 550025 Peoples R China

chicken swarm optimization (CSO) algorithm is one of very effective intelligence optimization algorithms, which has good performance in solving global optimization problems (GOPs). However, the CSO algorithm performs relatively poorly in complex GOPs for some weaknesses, which results the iteration easily fall into a local minimum. An improved chicken swarm optimization algorithm (ICSO) is proposed and applied in robot path planning. Firstly, an improved search strategy with Levy flight characteristics is introduced in the hen & x2019;s location update formula, which helps to increase the perturbation of the proposed algorithm and the diversity of the population. Secondly, a nonlinear weight reduction strategy is added in the chicken & x2019;s position update formula, which may enhance the chicken & x2019;s self-learning ability. Finally, multiple sets of unconstrained functions are used and a robot simulation experimental environment is established to test the ICSO algorithm. The numerical results show that, comparing to particle swarm optimization (PSO) and basic chicken swarm optimization (CSO), the ICSO algorithm has better convergence accuracy and stability for unconstrained optimization, and has stronger search capability in the robot path planning.

关键词： optimization Particle swarm optimization Convergence Robots Path planning Search problems Sociology chicken swarm optimization algorithm Levy flight nonlinear weight reduction strategy numerical experiments robot path planning

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A GOETHITE PROCESS MODELING METHOD BY ASYNCHRONOUS FUZZY COGNITIVE NETWORK BASED ON AN IMPROVED CONSTRAINED chicken swarm optimization algorithm

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JOURNAL OF INDUSTRIAL AND MANAGEMENT optimization 2021年第3期17卷 1269-1287页

作者： Peng, Junjie Chen, Ning Dai, Jiayang Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China

In order to solve the problem that the mechanism model of nonlinear system with uncertainty is difficult to establish, a modeling method of nonlinear system based on Asynchronous Fuzzy Cognitive Network (AFCN) is proposed. This method combines fuzzy cognitive network with time-lag system, and extends the node state values and weights of fuzzy cognitive network to the time interval, which enhances the adaptability of the model. At the same time an improved constrained chicken swarm optimization algorithm(ICCSOA) is proposed to identify model parameters of AFCN. A lag matrix corresponding to the actual measured values of the system lag of the nodes in the AFCN model is introduced, and a correction term including the difference between the measured values and the predicted values of the system is added to the model parameter updating mechanism. The simulation experiment results of goethite process system shows this modeling method can be used to model complex systems with uncertainties or partial missing data. The control model based on the established system model can make correct control decisions. ICCSOA has the advantages of fast convergence speed and accurate learning results, whose global search ability and convergence accuracy are higher than those of CSO algorithm, which can be widely used to the modeling of uncertain systems.

关键词： Fuzzy cognitive network time-lag system chicken swarm optimization algorithm terminal constraint

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A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第7期57卷 170-170页

作者： Chen, Binhe Cao, Li Chen, Changzu Chen, Yaodan Yue, Yinggao Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Intelligent Informat Syst Inst Wenzhou 325035 Peoples R China

The application of optimization theory and the algorithms that are generated from it has increased along with science and technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial optimization issues. swarm intelligence optimization algorithms have been successful in machine learning, process control, and engineering prediction throughout the years and have been shown to be efficient in handling combinatorial optimization issues. An intelligent optimization system called the chicken swarm optimization algorithm (CSO) mimics the organic behavior of flocks of chickens. In the benchmark problem's optimization process as the objective function, it outperforms several popular intelligent optimization methods like PSO. The concept and advancement of the flock optimization algorithm, the comparison with other meta-heuristic algorithms, and the development trend are reviewed in order to further enhance the search performance of the algorithm and quicken the research and application process of the algorithm. The fundamental algorithm model is first described, and the enhanced chicken swarm optimization algorithm based on algorithm parameters, chaos and quantum optimization, learning strategy, and population diversity is then categorized and summarized using both domestic and international literature. The use of group optimization algorithms in the areas of feature extraction, image processing, robotic engineering, wireless sensor networks, and power. Second, it is evaluated in terms of benefits, drawbacks, and application in comparison to other meta-heuristic algorithms. Finally, the direction of flock optimization algorithm research and development is anticipated.

关键词： swarm intelligence Evolutionary computation Bio-inspired algorithm chicken swarm optimization algorithm

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Adaptive chicken swarm optimization algorithm for identifying structural parameters of 6-DOF mechanical arm

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第1期46卷 1-12页

作者： Xu, Zhiqiang Xia, Junyong Zhong, Fei Hubei Univ Technol Sch Mech Engn Wuhan 430068 Hubei Peoples R China

Accurately identifying the structural parameters is a crucial way to increase the accuracy of the mechanical arm. A novel means combining the single-point conical hole repeatability experiment and meta-heuristic algorithms is proposed. Firstly, the kinematic model of the mechanical arm is constructed by adopting the modified Denavit-Hartenberg (MDH) method. Secondly, to overcome the difficulty of relying on the high-precision equipment in the identification process, an objective function by means of the kinematic model cleverly transforms this difficulty into a function optimization problem. Thirdly, a modified chicken swarm optimization algorithm (mCSO) focusing on dynamically and adaptively changing moving steps of chickens is designed to tackle the issue of the low convergence accuracy. Then, mCSO and other meta-heuristic algorithms are exploited to figure out the objective function to obtain the identified structural parameters. Finally, to prove and test the availability and reliability of the approach, the single-point conical hole repeatability experiment is conducted by using the mechanical arm before and after identification. The experimental result reveals that the novel means is efficacious and practicable.

关键词： 6-DOF mechanical arm Structural parameters Single-point conical hole repeatability experiment Adaptability chicken swarm optimization algorithm

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chicken swarm optimization algorithm based on quantum behavior and its convergence analysis

Chicken swarm optimization algorithm based on quantum behavi...

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第三十九届中国控制会议

作者： Zhang Qiuqiao Bing Wang Lingyan Wei Wang Haishan College of Energy and Electrical Engineering Hohai University

Aiming at the defects of chicken swarm optimization algorithm, such as easy to fall into local optimal, premature convergence and slow convergence, a chicken swarm optimization algorithm based on quantum behavior is proposed in this paper. A quantized potential well model is established based on the individual information of chicken swarm. According to the existing individual extremum and global extremum obtained by the original updating formula, Monte Carlo random sampling is adopted to complete the updating of individual extremum, and the search is conducted at a parallel Angle near individual extremum and global extremum, which improves the local search performance of the algorithm. At the same time, the convergence of quantum-behavior chicken swarm optimization algorithm is discussed in this paper, and QCSO is proved to be a globally convergent optimization algorithm. The optimization capability of QCSO is tested by using basic test function, and the results show that the optimization performance of this algorithm is greatly improved compared with the original algorithm.

关键词： global convergence quantum behavior chicken swarm optimization algorithm benchmark functions

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NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-38页

作者： Huang, Huajuan Zheng, Baofeng Wei, Xiuxi Zhou, Yongquan Zhang, Yuedong Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Minzu Univ Coll Elect Informat Nanning 530006 Peoples R China Guangxi Minzu Univ Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems.

关键词： Multi-objective optimization Meta-heuristic chicken swarm optimization algorithm Fast non-dominated sorting Multi-objective engineering design problems

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Artificial chicken swarm algorithm for multi-objective optimization with deep learning

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JOURNAL OF SUPERCOMPUTING 2021年第11期77卷 13069-13089页

作者： Wei, Qianzhou Huang, Dongru Zhang, Yu Guangdong Inst Intelligent Mfg Guangdong Key Lab Modern Control Technol Guangzhou Peoples R China Univ Finance & Econ Sch Publ Finance & Taxat Guangdong Guangzhou Peoples R China

With the rapid development of computer hardware in the past three decades, various classic algorithms such as neural computing and bionic optimization computing have been widely used in practical problems. This paper extended the new bionic algorithm-flock algorithm proposed in 2014 and obtained a multi-objective flock algorithm to solve the multi-objective problem. This study used aggregate functions to define social ranks, and simulated the foraging behavior of chickens in the process of searching for food in the objective space and found the balance between diversity and convergence when looking for the best Pareto solution. The algorithm took five types of bi-objective functions and four types of three-objective functions as objects and compared it with four more widely used algorithms in multi-objective problems. The results demonstrate that the MOCSO (multi-objective chicken swarm optimization) algorithm shows better results in the optimization of multi-objective problems.

关键词： Neural computing Bionic optimization calculation Multi-objective optimization chicken swarm optimization algorithm Pareto solution set Deep learning

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A new image segmentation method based on the ICSO-ISPCNN model

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第37-38期79卷 28131-28154页

作者： Liang, Jianhui Wang, Lifang Ma, Miao Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Hainan Univ Coll Appl Sci & Technol Danzhou 571737 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China

To address the issue of parameter settings in a pulse coupled neural network (PCNN), we propose a new image segmentation method based on the improved chicken swarm optimization algorithm and improved simplified PCNN (ICSO-ISPCNN) model. First, we improved a simplified PCNN model by modifying the dynamic threshold function and meanwhile improved the chicken swarm optimization (CSO) algorithm by introducing the survival of the fittest mechanism. Then, a product cross entropy is utilized as the fitness function of the ICSO algorithm, and the parameter values of the ISPCNN model are determined through the effective teamwork of roosters, hens, and chicks in the chicken swarm. Finally, we can achieve the automatic image segmentation via the ISPCNN model, which has the best parameter values. The detailed experiments indicate that our method has more superior performance in terms of convergence and segmentation accuracy than methods based on the genetic algorithm and ant colony optimization algorithm.

关键词： chicken swarm optimization algorithm Image segmentation Pulse coupled neural network

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