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quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits

NEUROCOMPUTING 2008年第1-3期72卷 581-591页

作者： Li, Panchi Li, Shiyong Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Heilongjiang Peoples R China Daqing Petr Inst Dept Comp Sci & Engn Daqing 163318 Peoples R China

A novel quantum-inspired evolutionary algorithm is proposed based on the Bloch coordinates of quantum bits (qubits) in this paper. The chromosome is comprised of qubits whose Bloch coordinates comprise gene chain. The quantum chromosomes are updated by quantum rotation gates, and are mutated by quantum non-gates. For the rotation direction of quantum rotation gates, a simple determining method is proposed. For the rotation and mutation of qubits, two new operators are constructed based on Bloch coordinates of qubits. In this algorithm, the Bloch coordinates of each qubit are regarded as three paratactic genes, each chromosome contains three gene chains, and each gene chain represents an optimization solution, which can accelerate the convergence process for the same number of chromosomes. By two application examples of function extremum and neural network weights optimization, the simulation results show that the approach is superior to common quantum evolutionary algorithm and simple genetic algorithm in both search capability and optimization efficiency. (c) 2007 Elsevier B.V. All rights reserved.

关键词： quantum-inspired evolutionary algorithm Bloch coordinates Three gene chains encoding quantum rotation gate Optimization algorithm

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quantum-inspired evolutionary algorithm Approach for Unit Commitment

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IEEE TRANSACTIONS ON POWER SYSTEMS 2009年第3期24卷 1503-1512页

作者： Lau, T. W. Chung, C. Y. Wong, K. P. Chung, T. S. Ho, S. L. Hong Kong Polytech Univ Dept Elect Engn Hong Kong Hong Kong Peoples R China Univ Western Australia Perth WA 6009 Australia

This paper presents a novel method for solving the unit commitment (UC) problem based on quantum-inspired evolutionary algorithm (QEA). The proposed method applies QEA to handle the unit-scheduling problem and the Lambda-iteration technique to solve the economic dispatch problem. The QEA method is based on the concept and principles of quantum computing, such as quantum bits, quantum gates and superposition of states. QEA employs quantum bit representation, which has better population diversity compared with other representations used in evolutionary algorithms, and uses quantum gate to drive the population towards the best solution. The mechanism of QEA can inherently treat the balance between exploration and exploitation and also achieve better quality of solutions, even with a small population. The proposed method is applied to systems with the number of generating units in the range of 10 to 100 in a 24-hour scheduling horizon and is compared to conventional methods in the literature. Moreover, the proposed method is extended to solve a large-scale UC problem in which 100 units are scheduled over a seven-day horizon with unit ramp-rate limits considered. The application studies have demonstrated the superior performance and feasibility of the proposed algorithm.

关键词： evolutionary algorithm quantum computing quantum-inspired evolutionary algorithm unit commitment

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Design of an improved quantum-inspired evolutionary algorithm for a transportation problem in logistics systems

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JOURNAL OF INTELLIGENT MANUFACTURING 2012年第6期23卷 2227-2236页

作者： Wang, Lixing Kowk, S. K. Ip, W. H. Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

Logistics faces great challenges in vehicle schedule problem. Intelligence Technologies need to be developed for solving the transportation problem. This paper proposes an improved quantum-inspired evolutionary algorithm (IQEA), which is a hybrid algorithm of quantum-inspired evolutionary algorithm (QEA) and greed heuristics. It extends the standard QEA by combining its principles with some heuristics methods. The proposed algorithm has also been applied to optimize a problem which may happen in real life. The problem can be categorized as a vehicle routing problem with time windows (VRPTW), which means the problem has many common characteristics that VRPTW has, but more constraints need to be considered. The basic idea of the proposed IQEA is to embed a greed heuristic method into the standard QEA for the optimal recombination of consignment subsequences. The consignment sequence is the order to arrange the vehicles for the transportation of the consignments. The consignment subsequences are generated by cutting the whole consignment sequence according to the values of quantum bits. The computational result of the simulation problem shows that IQEA is feasible in achieving a relatively optimal solution. The implementation of an optimized schedule can save much more cost than the initial schedule. It provides a promising, innovative approach for solving VRPTW and improves QEA for solving complexity problems with a number of constraints.

关键词： Optimization Heuristics quantum-inspired evolutionary algorithm Vehicle routing problem with time windows

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Hamming-distance-based adaptive quantum-inspired evolutionary algorithm for network coding resources optimization

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The Journal of China Universities of Posts and Telecommunications 2015年第3期22卷 92-99页

作者： Qu Zhijian Liu Xiaohong Zhang Xianwei Xie Yinbao Li Caihong School of Computer Science and Technology Shandong University of Technology

An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was taken into consideration, and a suitable rotation angle step （RAS） was assigned to each individual according to the Hamming distance. Performance comparisons were conducted among the HD-QEA, a basic quantum-inspired evolutionary algorithm （QEA） and an individual＇s fitness based adaptive QEA. A solid demonstration was provided that the proposed HD-QEA is better than the other two algorithms in terms of the convergence speed and the global optimization capability when they are employed to optimize the network coding resources in multicast networks.

关键词： network coding quantum-inspired evolutionary algorithm Hamming distance multicast network

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An effective hybrid quantum-inspired evolutionary algorithm for parameter estimation of chaotic systems

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第2期37卷 1279-1285页

作者： Wang, Ling Li, Ling-po Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China

Parameter estimation of chaotic systems is an important issue and has attracted increasing interest from a variety of research fields. Recently, quantum-inspired evolutionary algorithms have been proposed and applied to some optimization problems. However, to the best Of Our knowledge, there is no published research work oil quantum-inspired evolutionary algorithm (QEA) for estimating parameters of chaotic systems In this paper, ail effective hybrid quantum-inspired evolutionary algorithm with differential evolution (HQEDE) is proposed and applied to estimate the parameters of the Lorenz system. Numerical simulation and comparisons with other methods demonstrate the effectiveness and robustness of the proposed algorithm In addition. the effects of the parameter settings on HQEDE are investigated (C) 2009 Elsevier Ltd All rights reserved

关键词： Parameter estimation quantum-inspired evolutionary algorithm Differential evolution Hybrid algorithm Chaotic system

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Manoeuvring modelling of a containership in shallow water based on optimal truncated nonlinear kernel-based least square support vector machine and quantum-inspired evolutionary algorithm

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OCEAN ENGINEERING 2020年第0期195卷 106676-000页

作者： Xu, Haitong Guedes Soares, C. Univ Lisbon Inst Super Tecn Ctr Marine Technol & Ocean Engn CENTEC Av Rovisco Pais P-1049001 Lisbon Portugal

Nonlinear implicit models are proposed for manoeuvring simulation of a container model in shallow water. A series of Planar Motion Mechanism tests of the DTC ship model carried out in a towing tank with shallow water is used for training the nonlinear implicit manoeuvring model. A novel method, nonlinear kernel-based Least Square Support Vector Machine (LS-SVM), is proposed to approximate the nonlinear manoeuvring model. It is a robust method for regression modelling with a low computational cost by reducing the dimensionality of the kernel matrix. The radial basis function kernel is employed in the SVMs to guarantee the performance of the approximation. The quantum-inspired evolutionary algorithm (QEA) is used to search the optimal value of the predefined parameters of the nonlinear kernel-based LS-SVM. The optimal truncated LS-SVM is used to train the nonlinear regression models for ship manoeuvring, and the generalization performance of the obtained nonlinear manoeuvring models is further tested against the validation set. The R-2 goodness-of-fit criterion is used to demonstrate the accuracy of the obtained models.

关键词： Optimal truncated LS-SVM Shallow water manoeuvring quantum-inspired evolutionary algorithm Radial basis function kernel

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Short-term hydrothermal generation scheduling using differential real-coded quantum-inspired evolutionary algorithm

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ENERGY 2012年第1期44卷 657-671页

作者： Wang, Yongqiang Zhou, Jianzhong Mo, Li Zhang, Rui Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Changchun Inst Technol Dept Energy & Power Changchun 130012 Peoples R China

Short-term hydrothermal generation scheduling aims at determining optimal hydro and thermal generations to achieve minimum fuel cost of thermal plants for a 1 day or a 1 week while meeting various hydraulic and electric system constraints. The problem is viewed as a complex and nonlinear hard problem considering valve-points effects and transmission losses with a set of operation operational and physical constraints. This paper presents a novel effective differential real-coded quantum-inspired evolutionary algorithm (DRQEA) for solving this complicated problem. Some improvements like real-coded rule, adaptive differential mutation and crossover mechanism are proposed in DRQEA to enhance the global search ability in continuous space. Meanwhile, various constraints are handled effectively by using heuristic strategies designed by their characteristics. The effectiveness of the proposed approach is demonstrated on two hydrothermal test systems, which consist of two sub-systems: hydro sub-system and thermal sub-system. The obtained results of the proposed approach are compared with other methods, and simulation and comparison results clearly show that DRQEA is able to provide better solution than other reported methods, both in the solution quality and the convergence speed. The proposed algorithm can also apply to other dynamic optimization problem with nonlinear and non-convex characteristics in power system. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Hydrothermal generation scheduling quantum-inspired evolutionary algorithm Differential mutation Crossover Heuristic strategy

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Optimally robust H_∞ polynomial fuzzy controller design using quantum-inspired evolutionary algorithm

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2018年第12期49卷 2601-2617页

作者： Yu, Gwo-Ruey Huang, Yu-Chia Cheng, Chih-Yung Natl Chung Cheng Univ Dept Elect Engn 168 Univ Rd Chiayi 62102 Taiwan Natl Taiwan Ocean Univ Dept Elect Engn Keelung Taiwan

This paper proposes an optimally robust H polynomial fuzzy controller design using quantum-inspired evolutionary algorithm (QEA) for continuous/discrete time polynomial fuzzy systems with model uncertainties and external disturbances. To improve control performance, QEA is adopted to evolve optimal control gains with a fitness function that is defined by performance requirements. The stability and robustness of the control system are then guaranteed by the proposed robust H-infinity stability conditions, which are formed by the sum of squares (SOS) method. By using the principle of copositivity, novel relaxed SOS-based stability conditions are derived to reduce the conservativeness of solving SOS-based stability conditions, while the feasible solution space is broadened. Four numerical examples demonstrate the effectiveness of the proposed approaches.

关键词： Polynomial fuzzy control robust H-infinity criterion quantum-inspired evolutionary algorithm

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An Advanced quantum-inspired evolutionary algorithm for Unit Commitment

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IEEE TRANSACTIONS ON POWER SYSTEMS 2011年第2期26卷 847-854页

作者： Chung, C. Y. Yu, Han Wong, Kit Po Hong Kong Polytech Univ Dept Elect Engn Computat Intelligence Applicat Res Lab CIARLab Hong Kong Hong Kong Peoples R China N China Elect Power Univ Key Lab Power Syst Protect & Dynam Secur Monitori Minist Educ Beijing 102206 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Perth WA 6009 Australia Hong Kong Polytech Univ Power Syst Res Grp Hong Kong Hong Kong Peoples R China

Based on a quantum-inspired evolutionary algorithm for unit commitment, this paper proposed ways to advance the efficiency and robustness of the algorithm so that its capacity for application in large-scale unit commitment problems can be significantly enhanced. The paper develops an advanced quantum-inspired evolutionary unit commitment algorithm by developing a new initialization method based on unit priority list and a special Q-bit expression for ensuring diversity in the initial search area for improving the efficiency of solution searching. Different techniques such as multi-observation, single-search, and group-search are also proposed for incorporation in the advanced algorithm. The advanced algorithm is tested and compared with the earlier quantum-inspired evolutionary algorithm and a number of known methods through its applications to test systems with up to 100 generator units for a 24-h scheduling horizon.

关键词： Q-bit individual initialization quantum-inspired evolutionary algorithm single- and group-search unit commitment

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The quantum-inspired evolutionary algorithm in the Parametric Optimization of Lithium-Ion Battery Housing in the Multiple-Drop Test

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BATTERIES-BASEL 2024年第9期10卷 308页

作者： Ruranski, Adam Kus, Waclaw Silesian Tech Univ Fac Mech Engn PL-44100 Gliwice Poland

Recent developments in lithium-ion batteries have improved their capacity, which allows them to be used in more applications like power tools. However, they also carry higher risks, such as thermal runaway, which can happen if they are damaged. To make these batteries safer, it is important to improve the design of their housings subjected to multiple drops during their use. This article introduces a new method for optimizing the design of lithium-ion battery housings using a quantum-inspired evolutionary algorithm (QEA). Previously used mainly in theoretical settings, the authors have adapted QEA for practical engineering tasks. Multiple-drop test simulations were performed, and QEA was used to identify the best housing designs that minimize damage. To support this, a program was developed that automates all drop tests and rebuilds the model. The damage is obtained on the basis of the finite element method (FEM) analyses. The findings show that the algorithm successfully identified designs with the least damage during these tests. This research helps make battery housings safer and explores new uses for QEA in mechanical engineering.

关键词： quantum-inspired evolutionary algorithm optimization multiple-drop test FEM Li-ion battery housing

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