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15th International Symposium on Applied Electromagnetics and Mechanics (ISEM)

作者： Zhang, Wei Xu, Hailiang Bai, Yanan Yang, Shiyou Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

To eliminate the deficiency of traditional evolutionary algorithms in numerical implementations with respect to the three key operators such as selection, crossover and mutation operators, a new evolutionary algorithm based on probabilistic models, the quantum-inspired evolutionary algorithm (QEA), is studied for design optimizations of electromagnetic devices. In the proposed QEA, an adaptive update formulation is proposed for the rotation angle to balance the exploration and exploiting searches while the two level information sharing is simplified to one level one to facilitate the implementation of the proposed algorithm. Also, a new information sharing mechanism is introduced. The proposed algorithm is evaluated on an engineering inverse problem with promising results.

关键词： quantum-inspired evolutionary algorithm inverse problem evolutionary algorithm based on probabilistic models

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An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 171卷 114629-114629页

作者： Cai, Xing Zhao, Huimin Shang, Shifan Zhou, Yongquan Deng, Wu Chen, Huayue Deng, Wuquan Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Chongqing Univ Cent Hosp Dept Endocrinol Key Lab Biorheol Sci & TechnolMinist Educ Chongqing 400014 Peoples R China

In order to overcome the slow convergence speed, poor global search ability and difficult designing rotation angle of quantum-inspired evolutionary algorithm (QEA), an improved quantum-inspired cooperative co evolution algorithm based on combining the strategies of cooperative co-evolution, random rotation direction and Hamming adaptive rotation angle, namely MSQCCEA is proposed, which is employed to propose a new airport gate allocation optimization method in this paper. In the proposed MSQCCEA, the cooperative co evolution strategy is used to improve the global search capability. The random rotation direction strategy is developed to change the quantum evolution direction from one to two in order to avoid local optimal solution and realize the full search of the solution space. A new Hamming adaptive rotation angle strategy is designed to enable individuals to adaptively adjust the rotation angle according to the difference degree between the individual and the target individual, so as to improve the global search ability and convergence speed. A new airport gate allocation optimization method using MSQCCEA is realized to effectively allocate airport gates to the flights. Finally, the knapsack problem and the actual airport gate allocation problem are used to verify the effectiveness of the proposed MSQCCEA and gate allocation optimization method, respectively. The comparison experiment results demonstrate that the proposed MSQCCEA has faster convergence speed and higher convergence accuracy, and the proposed gate allocation optimization method takes on great potential to make decisions for actual airport management.

关键词： quantum-inspired evolutionary algorithm Cooperative co-evolutionary algorithm Multi-strategy Knapsack problem Airport gate allocation

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Parallel quantum-inspired evolutionary algorithms for community detection in social networks

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APPLIED SOFT COMPUTING 2017年 61卷 331-353页

作者： Gupta, Shikha Mittal, Stuti Gupta, Tamanna Singhal, Isha Khatri, Barkha Gupta, Ajay K. Kumar, Naveen Univ Delhi Shaheed Sukhdev Coll Business Delhi India Univ Delhi Dept Comp Sci Delhi India Univ Delhi Comp Ctr Delhi India

The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers. In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization. In recent years, programmable graphics processing units - GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA (R) compute unified device architecture (CUDA) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-e

关键词： Social network Community detection quantum-inspired evolutionary algorithm Parallel algorithm Network modularity

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Towards the right amount of randomness in quantum-inspired evolutionary algorithms

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SOFT COMPUTING 2017年第7期21卷 1765-1784页

作者： Patvardhan, C. Bansal, Sulabh Srivastav, Anand Dayalbagh Educ Inst Fac Engn Agra 282010 Uttar Pradesh India Christian Albrechts Univ Kiel Inst Informat D-24098 Kiel Germany 1-25 Hazuri Bhawan Agra Uttar Pradesh India

quantum-inspired evolutionary algorithms (QIEAs) combine the advantages of quantum-inspired bit (Q-bit), representation and operators with evolutionary algorithms for better performance. Using quantum-inspired representation the complete binary search space can be generated by collapsing a single Q-bit string repeatedly. Thus, even a population size of 1 can be taken in a QIEA implementation resulting in enormous saving in computation. Although this is correct in theory, QIEA implementations run into trouble in exploring large search spaces with this approach. The Q-bit string has to be initialized to produce each possible binary string with equal probability and then altered slowly to probabilistically favor generation of strings with better fitness values. This process is unacceptably slow when the search spaces are very large. Many ideas have been reported with EAs/QIEAs for speeding up convergence while ensuring that the algorithm does not get stuck in local optima. In this paper, the possible features are identified and systematically introduced and tested in the QIEA framework in various combinations. Some of these features increase the randomness in the search process for better exploration and the others compensate by local search for better exploitation together enabling a judicious combination tailored for particular problem being solved. This is referred to as "right-sizing the randomness" in the QIEA search. Benchmark instances of the well-known and well-studied Quadratic Knapsack Problem are used to demonstrate how effective these features are-individually and collectively. The new framework, dubbed QIEA-QKP, is shown to be much more effective than canonical QIEA. The framework can be utilized with profit on other problems and is being attempted.

关键词： evolutionary algorithm Combinatorial optimization quantum-inspired evolutionary algorithm Quadratic Knapsack Problem

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Efficient Solutions of the Density Classification Task in One-Dimensional Cellular Automata: Where Can They Be Found?

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COMPLEX SYSTEMS 2020年第3期29卷 669-688页

作者： Laboudi, Zakaria Univ Oum El Bouaghi RELA CS Lab 2 Oum El Bouaghi Algeria

The density classification task is one among other benchmark problems for studying the ability of cellular automata to solve problems through emergent collective computations. The density classification task attempts to find a local rule that can perform majority voting in an arbitrary initial configuration of a cellular automaton. Solutions for this problem were designed by means of several training mechanisms, more particularly optimization algorithms, due to the lack of standard procedures for selecting suitable local rules. In this paper, we propose new investigations into density determination in one-dimensional cellular automata of radius r = 4. We show that our proposal allows retaining a considerable number of unknown rules, some of which may outperform the current efficient solutions. Moreover, we give explanations about the computational mechanisms making global computations emerge so that the considered problem is solved. This is a key element for improving both the general understanding of the way in which computational tasks are solved by emergence and the selection of suitable local state-transitions rules.

关键词： complex systems emergent global computation cellular automata density determination symmetry property number-conserving property quantum-inspired evolutionary algorithm

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Resource Optimization for Elective Surgical Procedures Using quantum-inspired Genetic algorithms.

Resource Optimization for Elective Surgical Procedures Using...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Gonzalez, Rene Vellasco, Marley Figueiredo, Karla Pontificia Univ Catolica Rio de Janeiro Elect Engeneering Dept Rio de Janeiro Brazil Univ Estado Rio De Janeiro Computat Sci Dept Rio De Janeiro Brazil

ISBN: (纸本)9781450361118

Currently, Health Units in a large number of countries in the world present service demand that exceeds their real capacities. This problem causes the inevitable emergence of long waiting lists. The optimization of such waiting list is very challenging, due to the large number of resources that must be considered. This paper proposes a new model, based on a quantum-inspired evolutionary algorithm, to optimize the scheduling of for elective surgical procedures. The quantum-inspired evolutionary algorithm for Healthcare (QIEA-H) model, aims not only to designate the necessary resources to the patients in order to achieve the successful completion of the chirurgical procedure, but also to reduce the total time used to perform all surgeries and the number of surgeries out of term. For the validation of the proposed model, a waiting list of 2000 surgeries was created artificially and using a simulation tool also developed in this work. The model achieved a reduction in the time of all surgeries of up to 16.25% and the number of surgeries out of date of up to 13.04%.

关键词： quantum-inspired evolutionary algorithm Health Scheduling Optimization Genetic algorithms

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A Variable-Angle-Distance quantum evolutionary algorithm for 2D HP Model 4th

A Variable-Angle-Distance Quantum Evolutionary Algorithm for...

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4th International Conference on Cloud Computing and Security (ICCCS)

作者： Zheng, Yu Zhang, Zhenrong Fang, Wei Liu, Wenjie Nanjing Univ Informat Sci & Technol Sch Comp & Software Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Jiangsu Peoples R China

ISBN: (纸本)9783030000219;9783030000202

The computational simulations under the two dimensional hydrophobic-polar (2D-HP) model from protein's amino is a fundamental and challenging problems in computational biology. In this paper, we propose an improved quantum-inspired evolutionary algorithm based on variable angle-distance rotation strategy (QEA-VAR) for this NP-hard combinatorial protein folding problems. The QEA-VAR method is based on the concept and principles of quantum computing, such as quantum bits, quantum rotation gates and superposition of states. Comparing to the previously well-known evolutionary algorithm for the protein folding problem, QEA-VAR can find optimal or near-optimal energy structure from the benchmark sequences with a small simulating samples. Moreover, the proposed method's global convergence is faster than the other evolutionary algorithms. The application studies have demonstrated the superior performance and feasibility of the proposed algorithm for the protein folding problem.

关键词： quantum-inspired evolutionary algorithm quantum angle-distance rotation (2D-HP) hydrophobic-polar Protein folding

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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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quantum-inspired hybrid algorithm for integrated process planning and scheduling

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2018年第6期232卷 1105-1122页

作者： Liu, Mi Yi, Shuping Wen, Peihan Chongqing Univ Coll Mech Engn Shazheng St Chongqing 400044 Peoples R China

Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantum-inspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.

关键词： Integrated process planning and scheduling quantum-inspired evolutionary algorithm group leader optimization algorithm chaotic maps hybrid algorithm

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Interval multi-objective quantum-inspired cultural algorithms

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NEURAL COMPUTING & APPLICATIONS 2018年第3期30卷 709-722页

作者： Guo, Yi-nan Zhang, Pei Cheng, Jian Wang, Chun Gong, Dunwei China Univ Min & Technol Sch Informat & Elect Engn Xuzhou Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

It had been proved that the knowledge may promote more efficient evolution. Considering the knowledge defined in different form, we present interval multi-objective quantum-inspired cultural algorithms so as to effectively utilize the implicit information embodied in the evolution to promote more efficient search. It adopts the dual structure derived from cultural algorithm. In population space, the rectangle's height of each allele in real-encoding quantum individuals is calculated in terms of the possibility dominant rank, instead of the relative fitness values. Three kinds of crowding operators are defined, including the crowding distance of hypercube, the harmonic distance of hypercube and the coverage rate of hypercube to grids, to measure the crowding degree among evolutionary individuals. In belief space, the knowledge is used to guide selection and mutation operations of evolutionary individuals and the update operation of quantum individuals. The statistic simulation results for four benchmark functions indicate that the solutions obtained from the proposed algorithms more close to the true Pareto front uniformly and the uncertainty of non-dominant solutions is less. Furthermore, the knowledge extracted from the evolution plays a positive role in improving the convergence and distribution.

关键词： Knowledge The crowding degree The rectangle's height quantum-inspired evolutionary algorithm Interval multi-objective optimization

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