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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 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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Identifying influential spreaders in social networks: A two-stage quantum-behaved particle swarm optimization with Lévy flight

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Chinese Physics B 2024年第1期33卷 743-754页

作者：卢鹏丽揽继茂唐建新张莉宋仕辉朱虹羽 Wenzhou Engineering Institute of Pump&Valve Lanzhou University of TechnologyWenzhou 325100China School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.

关键词： social networks influence maximization metaheuristic optimization quantum-behaved particle swarm optimization Lévy flight

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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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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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Dynamic cyclic kitting part-feeding scheduling for mixed-model assembly line by a hybrid quantum-behaved particle swarm optimization

ROBOTIC INTELLIGENCE AND AUTOMATION

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ROBOTIC INTELLIGENCE AND AUTOMATION 2023年第3期43卷 267-289页

作者： Zhou, Binghai Huang, Yufan Tongji Univ Sch Mech Engn Shanghai Peoples R China

PurposeThe purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption. Design/methodology/approachHybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions. FindingsComputational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems. Research limitations/implicationsThe feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs. Originality/valueThe dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.

关键词： Hybrid part-feeding Segmented sub-line assignment Mixed-model assembly line Electric vehicles Multi-objective optimization quantum-behaved particle swarm optimization

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quantum-behaved particle swarm optimization Fault-Tolerant Control for Human Occupied Vehicle 14th

Quantum-Behaved Particle Swarm Optimization Fault-Tolerant C...

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14th International Conference on Intelligent Robotics and Applications (ICIRA)

作者： Zhang, Huapeng Zhu, Daqi Shanghai Maritime Univ Shanghai Engn Res Ctr Intelligent Maritime Search Haigang Ave 1550 Shanghai 201306 Peoples R China

ISBN: (纸本)9783030890926;9783030890919

In this paper, for the human occupied vehicle (HOV) system, the control strategies are used to reallocate the thruster forces based on quantum-behaved particle swarm optimization (QPSO). QPSO is adopted in terms of the solution quality, robustness and the convergence property. When the normalized thruster forces are out of maximum limits, the QPSO is used for the restricted usage of the faulty thruster and to find the solution of the control reallocation problem within the limits. An optimization criterion with the infinite norm as the cost function is introduced into the QPSO algorithm accelerate the search for the optimal solution in the feasibility space so as to ensure the feasibility of the solution. To show the efficiency of QPSO fault-tolerant control, GA, PSO and pseudo inverse method were conducted to study the proposed fault-tolerant control method. Experimental results showed the proposed fault-tolerant method could reallocate the thruster forces effective after thruster fault.

关键词： Human occupied vehicle quantum-behaved particle swarm optimization Thruster fault-tolerant control

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An adaptive binary quantum-behaved particle swarm optimization algorithm for the multidimensional knapsack problem

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swarm AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Li, Xiaotong Fang, Wei Zhu, Shuwei Zhang, Xin Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Comp Inte Wuxi Peoples R China

The multidimensional knapsack problem (MKP) is a classical combinatorial optimization problem with wide real -life applications. Binary quantum -behaved particle swarm optimization (BQPSO) algorithm is a popular heuristic algorithm used in binary optimization. While BQPSO exhibits strong global search capabilities, it is still prone to local optima due to particle aggregation. To address this issue, an adaptive BQPSO (ABQPSO) algorithm is proposed to solve the MKP efficiently. A hybrid encoding population initialization scheme is employed, leveraging specific knowledge of MKP to increase population diversity and improve search efficiency. Furthermore, ABQPSO uses a mapping strategy that converts continuous values into discrete values based on the average position of particles. An adaptive repair operator considering two pseudo -utility ratios introduced to enable particles to explore different feasible regions, which dynamically adjusts current pseudo utility ratios based on changes in the global best solution. A local search method is applied to guide particles towards convergence to the optimum. A local sparseness degree measurement and a diversity mechanism are utilized to avoid local optima. To evaluate the effectiveness of ABQPSO, it is compared against ten stateof-the-art algorithms using 168 MKP benchmark instances of varying scales. Experimental results reveal that ABQPSO outperforms the comparison algorithms, especially for large-scale problems, demonstrating better solution accuracy.

关键词： Multidimensional knapsack problem Combinatorial optimization problem quantum-behaved particle swarm optimization Adaptive repair operator Local search

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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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An improved binary quantum-behaved particle swarm optimization algorithm for knapsack problems

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INFORMATION SCIENCES 2023年第1期648卷

作者： Li, Xiaotong Fang, Wei Zhu, Shuwei Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi Peoples R China

The 0-1 knapsack problem is a typical NP-hard combinatorial optimization problem that is difficult to solve efficiently based on traditional optimization approaches. Binary quantum behaved particle swarm optimization (BQPSO) algorithm is a simple yet efficient discrete optimization approach since it guarantees global search. However, BQPSO suffers from local optimum and slow convergence problems due to aggregation among particles, limiting its performance. To address this problem, an improved BQPSO (IBQPSO) algorithm is proposed to effectively solve the 0-1 knapsack problem. First, to address the discretization issues in existing QPSO algorithms, a mapping strategy based on the average position of all particles is introduced. Then, the transfer function is used to discretize the local attractors into binary vectors for crossover operations, which can guide individuals in the discrete space to the optimum. In addition, a new repair method is employed to address infeasible solutions, and a diversity maintenance mechanism is developed in IBQPSO to alleviate the local optima problem. The proposed algorithm is compared with ten state-of-the-art heuristic algorithms on a set of 0-1 knapsack problems with different scales. Experimental results show that IBQPSO has obvious superiority over the other ten algorithms in terms of convergence speed and search performance.

关键词： Knapsack problem Combinatorial optimization problem quantum-behaved particle swarm optimization Transfer function Local attractor

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