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Multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization for expensive optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Li, Chao Zhang, Quanshu Palade, Vasile Lu, Hengyang Sun, Jun Jiangnan Univ Sch Artificial Intelligence & Comp Sci 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China Coventry Univ Ctr Computat Sci & Math Modelling Coventry CV1 2TL England

Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to solve computationally expensive optimization problems. However, most SAEAs struggle to achieve good results in solving complex multimodal problems, especially high-dimensional ones. Moreover, for problems with complex landscapes, SAEAs typically require constructing complex global surrogates to model the landscape and performing many iterations to identify the surrogate's optimum, thereby reducing the efficiency of SAEAs. To deal with these issues, this paper proposes a multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization (MHS-QPSO) algorithm for expensive optimization problems. To better balance exploration and exploitation, a search behavior selection strategy is proposed, enabling MHS-QPSO to appropriately switch between global and local searches. For the global search, the search space is divided into multiple regions that can adaptively adjust the size of the areas. A surrogate is constructed in each region, requiring only a small number of QPSO iterations to find the optimum of each surrogate. Furthermore, a novel reliability-based criterion is proposed to screen candidate solutions in different regions for exact evaluations, which can save the number of exact function evaluations and can rapidly improve the fitting accuracy of the surrogates in regions with superior fitness. During local searches, a dynamic boundary adjustment strategy is introduced to guide the QPSO to faster approach the potential optimal region. Experimental results on seven benchmark functions with dimensions from 10 to 100, and on a complex real application, demonstrate that MHS-QPSO significantly outperforms several state-of-the-art algorithms within a limited computational budget. Code for MHS-QPSO is available at https://***/quanshuzhang/***.

关键词： Surrogate-assisted evolutionary algorithm (SAEA) quantum-behaved particle swarm optimization (QPSO) Multi-region search Hierarchical optimization algorithm Reliability-based criterion

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quantum-behaved particle swarm optimization Algorithm Based on the Two-Body Problem

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Chinese Journal of Electronics 2019年第3期28卷 569-576页

作者： YAN Tao LIU Fengxian Institute of Big Data Science and Industry Shanxi University School of Computer and Information Technology Shanxi University Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Guangzhou Institute of Electronic Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

The present study proposes an improved quantum-behaved particle swarm optimization algorithm based on the two-body problem model(QTPSO) for solving the problem that other quantum-behaved particle swarm optimization algorithms easily converge on local optimal solutions when solving complex nonlinear problems. In the proposed QTPSO algorithm, particles are categorised as core particles and edge particles. Once the position of the core particle is determined, the edge particle appears in the vicinity of the attractor exhibiting a high probability, and the attractor is obtained through the random weighted sum of the core particle and the optimal mean position. Through simulation of the motion of these two particles by applying the interaction of the particles in the two-body problem, this mechanism not only improves the diversity of the population, but also enhances the local search capacity. To validate the proposed algorithm, three groups of experimental results were obtained to compare the proposed algorithm with other swarm intelligence algorithms. The experimental results indicate the superiority of the QTPSO algorithm.

关键词： quantum-behaved particle swarm optimization Two-body problem quantum potential well Wave function Nonlinear optimization problems

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quantum-behaved particle swarm optimization Based on Concentration Selection Probability Assignment Weights for Power System Economic Dispatch

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2024年第5期19卷 640-652页

作者： Wang, Miao Cao, Zhiao Li, Yuchun Northeastern Univ Sydney Smart Technol Coll 143 Taishan Rd Qinhuangdao 066004 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn 143 Taishan Rd Qinhuangdao 066004 Peoples R China Northeastern Univ Qinhuangdao Sch Comp & Commun Engn 143 Taishan Rd Qinhuangdao 066004 Peoples R China

The economic dispatch (ED) problem is very important in the economics of power systems, and a suitable dispatch algorithm can help power plants save huge amounts of money and use energy efficiently. To ensure the economics of the power system dispatch strategy, this paper proposes a quantum-behaved particle swarm improvement algorithm for solving the ED problem. First, the algorithm addresses the problem that the classical quantum-behaved particle swarm optimization (QPSO) tends to be premature and falls into local optimality. We propose a method to calculate the mean best position by assigning weights to different particles, by adopting the idea of concentration selection in the immune concentration regulation mechanism, and designing a Weight-Concentration selection probability function, so that those particles located outside the local optimal solution region but with smaller fitness value and better evolutionary trend can be retained. Second, to make the algorithm focus on global search at the beginning of the iteration and more focused search at the end to enhance the neighborhood search capability at the later stage, a decreasing function of the slope K in the Weight-Concentration selection probability function is designed so that the influence of the weighting term on the calculation of the mean best position decreases gradually. The algorithm is evaluated with four power systems, and the results show that the overall performance of the improved algorithm is better than other algorithms in solving the ED problem in terms of minimum solution, convergence, and stability. (c) 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

关键词： quantum-behaved particle swarm optimization economic dispatch (ED) concentration selection weighted mean best position local optimality

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quantum-behaved particle swarm optimization algorithm for the reconstruction of fiber Bragg grating sensor strain profiles

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OPTICS COMMUNICATIONS 2012年第5期285卷 539-545页

作者： Zou, Hongbo Liang, Dakai Zeng, Jie Feng, Lihang Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Jiangsu Peoples R China Three Gorges Univ Coll Elect Engn & Renewable Energy Yichang 443002 Peoples R China

A quantum-behaved particle swarm optimization algorithm used for demodulating the strain profile along a fiber Bragg grating (FBG) from its reflection spectrum has been demonstrated. By combining the transfer matrix method for calculating the reflection spectrum of a FBG and the quantum-behaved particle swarm optimization technique, we develop a new technique for the reconstruction of FBG sensor strain profiles. This proposed technique is verified through numerical example reconstructions of FBG sensor simulated strain profile cases and numerical simulations reveal good agreement between the original and the reconstructed strain profiles. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Fiber Bragg grating particle swarm optimization quantum-behaved particle swarm optimization Strain distribution

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quantum-behaved particle swarm optimization-based active noise control system with timing varying path

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2021年第1期40卷 524-539页

作者： Yu, Ning Li, Zhaoxia Wu, Yinfeng Feng, Renjian Beihang Univ Sch Instrumentat & Optoelect Engn Beijing Peoples R China

Active noise control systems can effectively suppress the impact of low-frequency noise and they have been applied in many fields. Recently, the evolutionary computation algorithm-based active noise control system has attracted considerable attention. To improve the noise reduction performance of the evolutionary computation algorithm-based active noise control system and solve the problem that the system cannot converge again when the path abruptly changes in steady state, we propose the path abruptly change-quantum-behaved particle swarm optimization algorithm. We apply quantum-behaved particle swarm optimization, a global optimization algorithm, to the active noise control system to improve noise reduction performance. In addition, the scheme of detecting the abrupt path change in steady state and performing re-convergence processing is designed to effectively address the problem that the system cannot regain convergence after a path change in steady state. The simulation study demonstrates that the proposed algorithm can efficiently improve noise reduction performance, accurately detect the path change, and re-converge to new global optimization.

关键词： Active noise control path change quantum-behaved particle swarm optimization

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quantum-behaved particle swarm optimization with collaborative attractors for nonlinear numerical problems

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2017年 44卷 167-183页

作者： Liu, Tianyu Jiao, Licheng Ma, Wenping Shang, Ronghua Xidian Univ Int Res Ctr Intelligent Percept & ComputatMinist Joint Int Res Lab Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Provinc Peoples R China

In this paper, an improved quantum-behaved particle swarm optimization (CL-QPSO), which adopts a new collaborative learning strategy to generate local attractors for particles, is proposed to solve nonlinear numerical problems. Local attractors, which directly determine the convergence behavior of particles, play an important role in quantum-behaved particle swarm optimization (QPSO). In order to get a promising and efficient local at tractor for each particle, a collaborative learning strategy is introduced to generate local attractors in the proposed algorithm. Collaborative learning strategy consists of two operators, namely orthogonal operator and comparison operator. For each particle, orthogonal operator is used to discover the useful information that lies in its personal and global best positions, while comparison operator is used to enhance the particle's ability of jumping out of local optima. By using a probability parameter, the two operators cooperate with each other to generate local attractors for particles. A comprehensive comparison of CLQPSO with some state-of-the-art evolutionary algorithms on nonlinear numeric optimization functions demonstrates the effectiveness of the proposed algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： quantum-behaved particle swarm optimization Local attractor Collaborative learning Function optimization

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quantum-behaved particle swarm optimization with Cooperative Coevolution for Large Scale optimization 14

Quantum-behaved Particle Swarm Optimization with Cooperative...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Tian, Na Jiangnan Univ Dept Educ Technol Wuxi 214122 Peoples R China

ISBN: (纸本)9781467365932

quantum-behaved particle swarm optimization (QPSO) has successfully been applied to unimodal and multimodal optimization problems. However, with the emerging and popular of big data and deep machine learning, QPSO encounters limitations with high dimensions. In this paper, QPSO with cooperative coevolution (QPSO_CC) is used to decompose the high dimensional problems into several lower dimensional problems and optimize them separately. The numerical experimental results show that QPSO_CC has comparative or even better performance than other algorithms.

关键词： large scale quantum-behaved particle swarm optimization cooperative coevolution domain decompositimponent

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quantum-behaved particle swarm optimization Based on Diversity-Controlled 13

Quantum-Behaved Particle Swarm Optimization Based on Diversi...

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13th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society (I3E)

作者： Long, HaiXia Fu, Haiyan Shi, Chun Hainan Normal Univ Sch Informat Sci Technol Haikou 571158 Hainan Peoples R China

ISBN: (纸本)9783662455265;9783662455258

quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. But QPSO algorithm is to be easily trapped into local optima as a result of the rapid decline in diversity. So this paper describes diversity-controlled into QPSO (QPSO-DC) to enhance the diversity of particle swarm, and then improve the search ability of QPSO. The experiment results on benchmark functions show that QPSO-DC has stronger global search ability than QPSO and standard PSO.

关键词： global convergence quantum-behaved particle swarm optimization diversity-controlled benchmark function

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quantum-behaved particle swarm optimization with dynamic grouping searching strategy

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INTELLIGENT DATA ANALYSIS 2023年第3期27卷 769-789页

作者： You, Qi Sun, Jun Palade, Vasile Pan, Feng Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China Coventry Univ Ctr Computat Sci & Math Modeling Coventry W Midlands England

The quantum-behaved particle swarm optimization (QPSO) algorithm, a variant of particle swarm optimization (PSO), has been proven to be an effective tool to solve various of optimization problems. However, like other PSO variants, it often suffers a premature convergence, especially when solving complex optimization problems. Considering this issue, this paper proposes a hybrid QPSO with dynamic grouping searching strategy, named QPSO-DGS. During the search process, the particle swarm is dynamically grouped into two subpopulations, which are assigned to implement the exploration and exploitation search, respectively. In each subpopulation, a comprehensive learning strategy is used for each particle to adjust its personal best position with a certain probability. Besides, a modified opposition-based computation is employed to improve the swarm diversity. The experimental comparison is conducted between the QPSO-DGS and other seven state-of-art PSO variants on the CEC'2013 test suit. The experimental results show that QPSO-DGS has a promising performance in terms of the solution accuracy and the convergence speed on the majority of these test functions, and especially on multimodal problems.

关键词： quantum-behaved particle swarm optimization premature convergence exploration exploitation

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quantum-behaved particle swarm optimization based on immune memory and vaccination

Quantum-behaved Particle Swarm Optimization based on immune ...

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IEEE International Conference on Granular Computing

作者： Liu, Jing Sun, Jun Xu, W. B. Kong, X. H. Southern Yangtze Univ Sch Informat Jiangsu Peoples R China

ISBN: (纸本)142440133X

The immune operation based on the regulation of the antibody thickness and vaccination operation in immune system is introduced into quantum-behaved particle swarm optimization, and then the quantum-behaved particle swarm optimization with immune operator is proposed in this paper. The results of typical optimization function showed that QPSO with immune operator performs much better than PSO and QPSO without immune operator.

关键词： immune memory particle swarm optimization quantum-behaved particle swarm optimization vaccination

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