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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Chen, Yujie Fan, Mingjie Zhao, Xinchao Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Particle swarm optimization (PSO) is a classic algorithm in the field of swarm intelligence. Despite its widespread use, PSO faces challenges in complex optimization scenarios, particularly its propensity for falling into local optima and slow convergence. comprehensive learning particle swarm optimization (CLPSO) has enhanced PSO's global search capabilities by introducing a comprehensive learning mechanism. This study presents further enhancements by proposing the dual population adaptive strategy comprehensive learning particle swarm optimization (DPAS-CLPSO), which combines a dual population framework with an adaptive learning paradigm to increase population heterogeneity and algorithm search efficiency. Experimental results on the CEC2017 standard benchmark tests have shown that DPAS-CLPSO significantly outperforms several well-known algorithms in both 30D and 50D problem spaces. Lastly, the effectiveness of the algorithm in solving complex optimization problems is also underscored by statistical validation through rank sum tests.

关键词： Particle swarm optimization comprehensive learning Dual population strategy Adaptive learning strategy

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comprehensive learning Jaya algorithm for engineering design optimization problems

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JOURNAL OF INTELLIGENT MANUFACTURING 2022年第5期33卷 1229-1253页

作者： Zhang, Yiying Jin, Zhigang Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Jaya algorithm (JAYA) is a recently developed metaheuristic algorithm for global optimization problems. JAYA has a very simple structure and only needs the essential population size and terminal condition for solving optimization problems. However, JAYA is easy to get trapped in the local optimum for solving complex global optimization problems due to its single learning strategy. Motivated by this disadvantage of JAYA, this paper presents an improved JAYA, named comprehensive learning JAYA algorithm (CLJAYA), for solving engineering design optimization problems. The core idea of CLJAYA is the designed comprehensive learning mechanism by making full use of population information. The designed comprehensive learning mechanism consists of three different learning strategies to improve the global search ability of JAYA. To investigate the performance of CLJAYA, CLJAYA is first evaluated by the well-known CEC 2013 and CEC 2014 test suites, which include 50 multimodal test functions and eight unimodal test functions. Then CLJAYA is employed to solve five real-world engineering optimization problems. Experimental results demonstrate that CLJAYA can achieve better solutions for most test problems than JAYA and the other compared algorithms, which indicates the designed comprehensive learning mechanism is very effective. In addition, the source code of the proposed CLJAYA can be loaded from .

关键词： Jaya algorithm comprehensive learning Metaheuristic algorithm Engineering optimization

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Discrete comprehensive learning particle swarm optimization algorithm with Metropolis acceptance criterion for traveling salesman problem

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 42卷 77-88页

作者： Zhong, Yiwen Lin, Juan Wang, Lijin Zhang, Hui Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou 350002 Fujian Peoples R China Fujian Prov Univ Fujian Agr & Forestry Univ Key Lab Smart Agr & Forestry Fuzhou 350002 Fujian Peoples R China Univ Louisville JB Speed Sch Engn Louisville KY 40292 USA

Particle swarm optimization (PSO) algorithm, one of the most popular swarm intelligence algorithms, has been widely studied and applied to a large number of continuous and discrete optimization problems. In this paper, a discrete comprehensive learning PSO algorithm, which uses acceptance criterion of simulated annealing algorithm, is proposed for Traveling Salesman Problem (TSP). A new flight equation, which can learn both from personal best of each particle and features of problem at hand, is designed for TSP problem. Lazy velocity, which is calculated in each dimension only when needed, is proposed to enhance the effectiveness of velocity. Eager evaluation, which evaluates each intermediate solution after velocity component is applied to the solution, is proposed to search the solution space more finely. Aiming to enhance its ability to escape from premature convergence, particle uses Metropolis acceptance criterion to decide whether to accept newly produced solutions. Systematic experiments were carried to show the advantage of the new flight equation, to verify the necessity to use non-greedy acceptance strategy for keeping sufficient diversity, and to compare lazy velocity and eager velocity. The comparison, carried on a wide range of benchmark TSP problems, has shown that the proposed algorithm is better than or competitive with many other state-of-the-art algorithms.

关键词： Particle swarm optimization Traveling salesman problem comprehensive learning Metropolis acceptance criterion Lazy velocity Eager evaluation

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Adaptive comprehensive learning particle swarm optimization with cooperative archive

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APPLIED SOFT COMPUTING 2019年 77卷 533-546页

作者： Lin, Anping Sun, Wei Yu, Hongshan Wu, Guohua Tang, Hollgwe Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China Hunan Key Lab Intelligent Robot Technol Elect Mfg Changsha 410082 Hunan Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Informat Syst & Management Changsha 410082 Hunan Peoples R China

comprehensive learning particle swarm optimization (CLPSO) enhances its exploration capability by exploiting all other particles' historical information to update each particle's velocity. However, CLPSO adopts a set of fixed comprehensive learning (CL) probabilities to learn from other particles, which may impair its performance on complex optimization problems. To improve the performance and adaptability of CLPSO, an adaptive mechanism for adjusting CL probability and a cooperative archive (CA) are combined with CLPSO, and the resultant algorithm is referred to as adaptive comprehensive learning particle swarm optimization with cooperative archive (ACLPSO-CA). The adaptive mechanism dividing the CL probability into three levels and adjusting the individual particle's CL probability level dynamically according to the performance of the particles during the optimization process. The cooperative archive is employed to provide additional promising information for ACLPO-CA and itself is updated by the cooperative operation of the current swarm and archive. To evaluate the performance of ACLPSO-CA, ACLPSO-CA is tested on CEC2013 test suite and CEC2017 test suite and compared with seven popular PSO variants. The test results show that ACLPSO-CA outperforms other comparative PSO variants on the two CEC test suites. ACLPSO-CA achieves high performance on different types of benchmark functions and exhibits high adaptability as well. In the end, ACLPSO-CA is further applied to a radar system design problem to demonstrate its potential in real-life optimization. (C) 2019 Elsevier B.V. All rights reserved.

关键词： comprehensive learning Particle swarm optimization Cooperative archive Radar system design

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Multilevel thresholding Aerial image segmentation using comprehensive learning-based Snow ablation optimizer with double attractors

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EGYPTIAN INFORMATICS JOURNAL 2024年 27卷

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Ibrahim, Rehab Ali Ewees, Ahmed A. Shrahili, Mansour Zagazig Univ Fac Sci Dept Math Zagazig Egypt Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Peoples R China Zhejiang Inst Optoelect Jinhua 321004 Peoples R China Damietta Univ Dept Comp Dumyat 34517 Egypt King Saud Univ Coll Sci Dept Stat & Operat Res POB 2455 Riyadh 11451 Saudi Arabia Emirates Int Univ Coll Engn & Informat Technol Sanaa 16881 Yemen Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Middle East Univ MEU Res Unit Amman 11831 Jordan

Aerial photography is a remote sensing technique used for target detection, enabling both qualitative and quantitative analysis. The segmentation process is considered one of the most important processes to improve the analysis of Aerial images. In this study, we introduce an alternative multilevel threshold image segmentation based on a modified Snow ablation optimizer (SAO) algorithm. This modification is conducted using the strengths of comprehensive learning and Double attractors which aims to enhance the exploration and exploitation abilities of the SAO during the process of discovering the optimal threshold levels that are used to segment the Aerial photography image. To validate the quality of the modified version of SAO, named DCSAO, a set of experimental series is conducted using the CEC2022 benchmark function and sixteen Aerial images at different threshold levels. In addition, we compared the results of DCSAO with different wellknown Metaheuristic techniques. The results show the superior performance of DCSAO in comparison to other algorithms according to the performance metrics.

关键词： Aerial image segmentation Snow ablation optimizer comprehensive learning Double attractors

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Levy flight-based inverse adaptive comprehensive learning particle swarm optimization

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第5期19卷 5241-5268页

作者： Zhou, Xin Zhou, Shangbo Han, Yuxiao Zhu, Shufang Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400030 Peoples R China

In the traditional particle swarm optimization algorithm, the particles always choose to learn from the well-behaved particles in the population during the population iteration. Nevertheless, according to the principles of particle swarm optimization, we know that the motion of each particle has an impact on other individuals, and even poorly behaved particles can provide valuable information. Based on this consideration, we propose Levy flight-based inverse adaptive comprehensive learning particle swarm optimization, called LFIACL-PSO. In the LFIACL-PSO algorithm, First, when the particle is trapped in the local optimum and cannot jump out, inverse learning is used, and the learning step size is obtained through the Levy flight. Second, to increase the diversity of the algorithm and prevent it from prematurely converging, a comprehensive learning strategy and Ring-type topology are used as part of the learning paradigm. In addition, use the adaptive update to update the acceleration coefficients for each learning paradigm. Finally, the comprehensive performance of LFIACL-PSO is measured using 16 benchmark functions and a real engineering application problem and compared with seven other classical particle swarm optimization algorithms. Experimental comparison results show that the comprehensive performance of the LFIACL-PSO outperforms comparative PSO variants.

关键词： particle swarm optimization inverse learning Levy flight comprehensive learning

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A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第2期34卷 1341-1350页

作者： Maltra, Madhubanti Chatterjee, Amitava Jadavpur Univ Dept Elect Engn Kolkata 700032 India

A novel optimal multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm presents an improved variant of PSO, a relatively recently introduced stochastic optimization strategy. This hybrid approach employs both cooperative learning and comprehensive learning along with some additional modifications. Cooperative learning is employed to overcome the "curse of dimensionality" by decomposing a high-dimensional swarm into several one-dimensional swarms. The comprehensive learning is then employed to discourage premature convergence in each one-dimensional swarm. The capability of this hybrid PSO (called HCOCLPSO) is further enhanced by cloning of fitter particles, at the expense of worst particles, determined on the basis of their fitness values. The performance of HCOCLPSO algorithm is evaluated vis-A-vis an improved GA-based algorithm [Yin, P.-Y., (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing 72, 85-95], Gaussian-smoothing method [Lim, Y. K., & Lee, S. U., (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern recognition. 23, 935-952;Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters 16, 653-666] and symmetry/duality method [Yin, P. Y., & Chen, L. H., (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging 2, 337-344] for several benchmark images and HCOCLPSO outperforms each of these algorithms for each such image. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： PSO cooperative learning comprehensive learning HCOCLPSO image segmentation multilevel thresholding

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Enhanced comprehensive learning particle swarm optimization

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APPLIED MATHEMATICS AND COMPUTATION 2014年 242卷 265-276页

作者： Yu, Xiang Zhang, Xueqing Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China

comprehensive learning particle swarm optimization (CLPSO) is a state-of-the-art metaheuristic that encourages a particle to learn from different exemplars on different dimensions. It is able to locate the global optimum region for many complex multimodal problems as it is excellent in preserving the particles' diversity and thus preventing premature convergence. However, CLPSO has been noted for low solution accuracy. This paper proposes two enhancements to CLPSO. First, a perturbation term is added into each particle's velocity update procedure to achieve high performance exploitation. Normative knowledge about dimensional bounds of personal best positions is used to appropriately activate the perturbation based exploitation. Second, the particles' learning probabilities are determined adaptively based on not only rankings of personal best fitness values but also the particles' exploitation progress to facilitate convergence. Experiments conducted on various benchmark functions demonstrate that the two enhancements successfully overcome the low solution accuracy weakness of CLPSO. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Global optimization Particle swarm optimization comprehensive learning

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A comprehensive learning based swarm optimization approach for feature selection in gene expression data

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HELIYON 2024年第17期10卷 e37165页

作者： Easwaran, Subha Venugopal, Jothi Prakash Subramanian, Arul Antran Vijay Sundaram, Gopikrishnan Naseeba, Beebi Karpagam Coll Engn Dept Sci & Humanities Coimbatore 641032 Tamil Nadu India Karpagam Coll Engn Dept Informat Technol Coimbatore 641032 Tamil Nadu India Karpagam Coll Engn Dept Comp Sci & Engn Coimbatore 641032 Tamil Nadu India VIT AP Univ Sch Comp Sci & Engn Amaravathi 522241 Andhra Pradesh India

Gene expression data analysis is challenging due to the high dimensionality and complexity of the data. Feature selection, which identifies relevant genes, is a common preprocessing step. We propose a comprehensive learning-Based Swarm Optimization (CLBSO) approach for feature selection in gene expression data. CLBSO leverages the strengths of ants and grasshoppers to efficiently explore the high-dimensional search space. Ants perform local search and leave pheromone trails to guide the swarm, while grasshoppers use their ability to jump long distances to explore new regions and avoid local optima. The proposed approach was evaluated on several publicly available gene expression datasets and compared with state-of-the-art feature selection methods. CLBSO achieved an average accuracy improvement of 15% over the original high-dimensional data and outperformed other feature selection methods by up to 10%. For instance, in the Pancreatic cancer dataset, CLBSO achieved 97.2% accuracy, significantly higher than XGBoost-MOGA's 84.0%. Convergence analysis showed CLBSO required fewer iterations to reach optimal solutions. Statistical analysis confirmed significant performance improvements, and stability analysis demonstrated consistent gene subset selection across different runs. These findings highlight the robustness and efficacy of CLBSO in handling complex gene expression datasets, making it a valuable tool for enhancing classification tasks in bioinformatics.

关键词： comprehensive learning Feature selection Gene expression Gene selection Swarm intelligence Cancer classification

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A modified hybrid particle swarm optimization based on comprehensive learning and dynamic multi-swarm strategy

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SOFT COMPUTING 2024年第5期28卷 3879-3903页

作者： Wang, Rui Hao, Kuangrong Chen, Lei Liu, Xiaoyan Zhu, Xiuli Zhao, Chenwei Donghua Univ Minist Educ Coll Informat Sci & Technol Engn Res Ctr Digitized Text & Apparel Technol Shanghai 201620 Peoples R China

Particle swarm optimization (PSO) is a simple yet efficient population-based algorithm that handles various optimization problems. Nevertheless, diversity and convergence are two significant PSO limits, particularly when tackling challenging optimization issues. This paper develops a PSO with comprehensive learning and a modified dynamic multi-swarm strategy (CLDMSL-PSO) to solve these problems. In the beginning, each iteration of CLDMSL-PSO splits the total population into two subpopulations, one for exploration and the other for exploitation. The comprehensive learning (CL) strategy builds exemplars for the exploration subpopulation. The modified dynamic multi-swarm (DMS) strategy is equipped with the Quasi-Newton method to create the exploitation subpopulation. Second, a self-regulation nonlinear inertia weight, which considers the search level of different sub-swarms, is developed to accelerate the search speed in the early stage and strengthen the exploitation ability in the latter stage of the exploitation subpopulation. Third, the exploitation subpopulation uses a dynamic regrouping period parameter to regulate the frequency of information exchange among the sub-swarms. Finally, the Cauchy mutation is adopted to prevent falling into local optima during the search process. CLDMSL-PSO has been tested on extensive benchmark functions and a multifilament melt spinning process problem. Experimental results show that CLDMSL-PSO outperforms other state-of-art evolutionary algorithms on most optimization problems.

关键词： Particle swarm optimization Dynamic sub-swarms comprehensive learning Quasi-newton method

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