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A hybrid quantum-based PIO algorithm for global numerical optimization

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Science China(Information Sciences) 2019年第7期62卷 33-44页

作者： Boyi CHEN Hao LEI Haidong SHEN Yanbin LIU Yuping LU College of Astronautics Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing University of Aeronautics and Astronautics

A novel hybrid quantum-based pigeon-inspired optimization(PIO) algorithm for global numerical optimization is proposed to perceive deceptiveness and preserve diversity. In the proposed algorithm,the current best solution is regarded as a linear superposition of two probabilistic states, namely positive and deceptive. Through a quantum rotation gate, the positive probability is either enhanced or reset to balance exploration and exploitation. Simulation results reveal that the hybrid quantum-based PIO algorithm demonstrates an outstanding performance in global optimization owing to preserving diversity in the early evolution. As a result, the stability of the algorithm is enhanced so that the precision of optimization is improved statistically. The proposed algorithm is demonstrated to be effective for solving multimodal and non-convex problems in higher dimension with a smaller population size.

关键词： PIO global convergence numerical optimization QEA

Enhanced self-adaptive evolutionary algorithm for numerical optimization

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Journal of Systems Engineering and Electronics 2012年第6期23卷 921-928页

作者： Yu Xue YiZhuang Tianquan Ni Jian Ouyang ZhouWang School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 E R. China No.723 Institute of China Shipbuilding Industry Corporation Yangzhou 225001 E R. China Science and Technology on Electron-optic Control Laboratory Luoyang 471000 P. R. China

There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.

关键词： self-adaptive numerical optimization evolutionary al-gorithm stochastic search algorithm.

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SOC distribution-based modeling for lithium-ion battery for electric vehicles using numerical optimization

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Journal of Harbin Institute of Technology(New Series) 2011年第5期18卷 49-54页

作者：胡晓松孙逢春邹渊 National Engineering Laboratory for Electric Vehicles Beijing Institute of Technology

In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.

关键词： battery modeling SOC distribution numerical optimization lithium-ion battery electric vehicle

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Quasi-Developable B-Spline Surface Design with Control Rulings

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Journal of Computer Science & Technology 2022年第5期37卷 1221-1238页

作者：胡梓轩伯彭波张彩明 School of Computer Science and Technology Harbin Institute of TechnologyWeihai 264209China Shandong Co-Innovation Center of Future Intelligent Computing Yantai 264005China Shandong Province Key Laboratory of Digital Media Technology Shandong University of Finance and EconomicsJinan 250061China

We propose a method for generating a ruled B-spline surface fitting to a sequence of pre-defined ruling lines and the generated surface is required to be as-developable-as-possible.Specifically,the terminal ruling lines are treated as hard constraints.Different from existing methods that compute a quasi-developable surface from two boundary curves and cannot achieve explicit ruling control,our method controls ruling lines in an intuitive way and serves as an effective tool for computing quasi-developable surfaces from freely-designed rulings.We treat this problem from the point of view of numerical optimization and solve for surfaces meeting the distance error tolerance allowed in applications.The performance and the efficacy of the proposed method are demonstrated by the experiments on a variety of models including an application of the method for path planning in 5-axis computer numerical control(CNC)flank milling.

关键词： developable surface surface modeling B-spline surface numerical optimization

Technique for Multi-Pass Turning optimization Based on Gaussian Quantum-Behaved Bat Algorithm

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1575-1602页

作者： Shutong Xie Zongbao He Xingwang Huang Computer Engineering College Jimei UniversityXiamen361021China

The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field.The main objective of this operation is to minimize the unit production cost.This paper proposes a Gaussian quantum-behaved bat algorithm(GQBA)to solve the problem of multi-pass turning operation.The proposed algorithm mainly includes the following two improvements.The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification.The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of the quantum-behaved bats,thus performing a more accurate search and avoiding premature convergence.The performance of the presented GQBA is demonstrated through numerical benchmark functions and amulti-pass turning operation problem.Thirteen classical benchmark functions are utilized in the comparison experiments,and the experimental results for accuracy and convergence speed demonstrate that,in most cases,the GQBA can provide a better search capability than other algorithms.Furthermore,GQBA is applied to an optimization problem formulti-pass turning,which is designed tominimize the production cost while considering many practical machining constraints in the machining process.The experimental results indicate that the GQBA outperforms other comparison algorithms in terms of cost reduction,which proves the effectiveness of the GQBA.

关键词： Bat algorithm quantum behavior gaussian distribution numerical optimization multi-pass turning

Sequential quadratic programming enhanced backtracking search algorithm

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Frontiers of Computer Science 2018年第2期12卷 316-330页

作者： Wenting ZHAO Lijin WANG Yilong YIN Bingqing WANG Yuchun TANG School of Computer Science and Technology Shandong University Jinan 250101 China College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou 350002 China Research Center for Sectional and Imaging Anatomy Shandong University School of Medicine Jinan 250012 China

In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.

关键词： numerical optimization backtracking search algorithm sequential quadratic programming local search

Rotated neighbor learning-based auto-configured evolutionary algorithm

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Science China(Information Sciences) 2016年第5期59卷 26-38页

作者： Yuanjun LAILI Lin ZHANG Fei TAO Pingchuan MA School of Automation Science and Electrical Engineering Beihang University

More and more evolutionary operators have been integrated and manually configured together to solve a wider range of problems. Considering the very limited progress made on the automatic configuration of evolutionary algorithms(EAs), a rotated neighbor learning-based auto-configured evolutionary algorithm(RNLACEA) is presented. In this framework, multiple EAs are combined as candidates and automatically screened for different scenarios with a rotated neighbor structure. According to a ranking record and a group of constraints,the algorithms can be better scheduled to improve the searching efficiency and accelerate the searching pace.Experimental studies based on 14 classical EAs and 22 typical benchmark problems demonstrate that RNLACEA outperforms other six representative auto-adaptive EAs and has high scalability and robustness in solving different kinds of numerical optimization problems.

关键词： multiple evolutionary algorithms algorithm auto-configuration rotated neighbor structure hyperheuristic numerical optimization

Improving differential evolution with a new selection method of parents for mutation

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Frontiers of Computer Science 2016年第2期10卷 246-269页

作者： Yiqiao CAI Yonghong CHEN Tian WANG Hui TIAN College of Computer Science and Technology Huaqiao University Xiamen 361021 China

In differential evolution （DE）, the salient feature lies in its mutation mechanism that distinguishes it from other evolutionary algorithms. Generally, for most of the DE algorithms, the parents for mutation are randomly chosen from the current population. Hence, all vectors of population have the equal chance to be selected as parents without selective pressure at all. In this way, the information of population cannot be fully exploited to guide the search. To alleviate this drawback and improve the performance of DE, we present a new selection method of parents that attempts to choose individuals for mutation by utilizing the population information effectively. The proposed method is referred as fitnessand-position based selection （FPS）, which combines the fitness and position information of population simultaneously for selecting parents in mutation of DE. In order to evaluate the effectiveness of FPS, FPS is applied to the original DE algorithms, as well as several DE variants, for numerical optimization. Experimental results on a suite of benchmark functions indicate that FPS is able to enhance the performance of most DE algorithms studied. Compared with other selection methods, FPS is also shown to be more effective to utilize information of population for guiding the search of DE.

关键词： differential evolution mutation operator parents selection population information numerical optimization

Memetic computation based on regulation between neural and immune systems: the framework and a case study

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Science China(Information Sciences) 2010年第8期53卷 1519-1527页

作者： GONG MaoGuo , JIAO LiCheng, LIU Fang & YANG Jie Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an China

Lamarckian learning has been introduced into evolutionary computation to enhance the ability of local search. The relevant research topic, memetic computation, has received significant amount of interest. In this study, a novel memetic computational framework is proposed by simulating the integrated regulation between neural and immune systems. The Lamarckian learning strategy of simulating the unidirectional regulation of neural system on immune system is designed. Consequently, an immune memetic algorithm based on the Lamarckian learning is proposed for numerical optimization. The proposed algorithm combines the advantages of immune algorithms and mathematical programming, and performs well in both global and local search. The simulation results based on ten low-dimensional and ten high-dimensional benchmark problems show that the immune memetic algorithm outperforms the basic genetic algorithm-based memetic algorithm in solving most of the test problems.

关键词： memetic computation artificial immune system Lamarckian learning genetic algorithm numerical optimization