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quantum Teaching-Learning-Based Optimization algorithm for sizing optimization of skeletal structures with discrete variables

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STRUCTURES 2021年 32卷 1798-1819页

作者： Kaveh, A. Kamalinejad, M. Hamedani, K. Biabani Arzani, H. Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran Shahid Rajaee Teacher Training Univ Dept Civil Engn Tehran Lavizan Iran

In terms of optimization algorithm improvement, researchers utilize some strategies to establish the right balance between local and global search phases to escape from a local minimum in some NP-hard problems. The approach of quantum-behaved particles, as a novel strategy, uses uncertainty law and a distinct formulation obtained from solving the time-independent Schrodinger differential equation in the delta-potential-well function to update the solution candidates' positions. New formulation defines the local attractors as probable solutions between the best solution and the others to explore all solution space domain. Also, mentioned formulation employs the difference between the average solution and other ones as a new step size. Both local attractors and new step sizes guarantee diversification besides the inherent intensification. In the present paper, the teacher phase of the Teaching-Learning-Based Optimization algorithm (TLBO), which has been introduced recently, is improved using the formulation obtained from solving the time-independent Schrodinger equation to predict the probable positions of optimal solutions. The results show that QTLBO, an acronym for the quantum Teaching- Learning- Based Optimization, improves the stability and robustness of the TLBO by defining the quantum teacher phase. The two planner rigid frames and two trusses with strength and serviceability constraints are chosen to verify the quality and performance of QTLBO. Comparing the results obtained from the proposed algorithm with those of the standard version of the TLBO algorithm and other literature methods shows that QTLBO increases the chance of finding a better solution besides improving the statistical criteria compared to the current TLBO.

关键词： quantum-inspired evolutionary algorithm Teaching-Learning-Based Optimization Population-based algorithm Rigid frames quantum behaved particles quantum teacher

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quantum evolutionary algorithm hybridized with Enhanced colliding bodies for optimization

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STRUCTURES 2020年 28卷 1479-1501页

作者： Kaveh, A. Kamalinejad, M. Arzani, H. Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn POB 16846-13114 Tehran Iran Shahid Rajaee Teacher Training Univ Dept Civil Engn Tehran Lavizan Iran

New methods based on meta-heuristic approaches for solving hard problems have become efficient tools in many disciplines such as engineering, computer science, mathematics, and some other fields. quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. The original version of the quantum evolutionary algorithm (QEA) uses Q-bit individuals in binary code analogous to genes in the conventional genetic algorithm and the concept of quantum computing. Getting stuck in local optimum in some case studies is considered a weakness of primary QEA and makes access to the high standard deviation in statistical analysis. The idea of a combination of two or more methods can create a new approach with augmented features of all the combined methods. QECBO, as a high-level relay hybrid technique, is a new quantum version of the ECBO, which combines ECBO with the property of the quantum particle. QECBO gives quantum particle properties to colliding bodies of solution. The main idea of QECBO originates from the concept of quantum particle behavior in delta potential well and Schroddinger's uncertainty principle addition to Newtonian collision laws. It seems that the QECBO utilizes the rules of uncertainty inside classical mechanics. This paper is devoted to the assessment of the QECBO using some well-known benchmark optimization problems. In addition to less sensitive to population size, the quality of obtained results implies that QECBO utilizes an advanced approach to search solution space based on ECBO and balances exploration and exploitation concepts by using uncertainty laws. The inclination of the agents to the best solution in each iteration could provide a better chance to find an optimal solution. Also, efficient randomization due to uncertainty laws helps the algorithm to escape from the local optimum. Attained statistical results

关键词： quantum-inspired evolutionary algorithm Enhanced colliding bodies optimization Optimization Truss structures quantum behaved particles Delta potential well function

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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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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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A clonal real-coded quantum-inspired evolutionary algorithm with Cauchy mutation for short-term hydrothermal generation scheduling

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 1228-1240页

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

The short-term hydrothermal generation scheduling (SHGS) problem considered valve-point effects and transmission losses is a complicated problem which has the characteristics of nonlinear and non-convex. This paper presents a clonal real-coded quantum-inspired evolutionary algorithm (CRQEA) with Cauchy mutation for solving SHGS problem. In this proposed algorithm, real-coded rule is adopted for handling continuous variables. Meanwhile, clonal operator and Cauchy mutation are integrated into the proposed algorithm to avoid premature convergence. Moreover, heuristic strategies are designed for dealing with all kinds of constraints effectively. Finally, three test systems are employed to verify the feasible and effectiveness the proposed CRQEA. Compared with other reported methods, the obtained results clearly illustrates that CRQEA has better performances. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Hydrothermal system quantum-inspired evolutionary algorithm Clonal operator Cauchy mutation Heuristic strategy

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On the adoption of Metaheuristics for Solving 0-1 Knapsack Problems 12

On the adoption of Metaheuristics for Solving 0-1 Knapsack P...

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12th International Symposium on Parallel Architectures, algorithms and Programming (PAAP)

作者： Qiu, Yang Li, Haomiao Wang, Xiao Dib, Omar Wenzhou Kean Univ Dept Comp Sci Wenzhou 325060 Peoples R China

ISBN: (纸本)9781665496391

0-1 knapsack problem is a classical Non-deterministic Polynomial Hard (NP-Hard) problem in combinatorial optimization. It has a wide range of applications in real life, such as the distribution of goods in logistics companies, capital calculation, storage, and distribution, etc. This paper studies the adoption of three meta-heuristic approaches for solving the 0-1 knapsack problem. A novel quantum-inspired Tabu Search (QTS) that combines a classical Tabu Search (TS) and quantum-inspired evolutionary algorithm (QEA), Ant Colony swarm intelligence algorithm (ACO), and Genetic algorithm (GA) with augmented fitness function. Based on empirical analysis of computer simulations and comparative demonstrations, we show that applying such metaheuristic results in high efficiency in terms of the quality of obtained solutions, computational time, and robustness when dealing with the underlying 0-1 knapsack optimization problem.

关键词： 0-1 knapsack Tabu Search quantum-inspired evolutionary algorithm Genetic algorithm Ant Colony Optimization

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A quantum-inspired evolutionary Clustering algorithm

A Quantum-inspired Evolutionary Clustering Algorithm

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International Conference on Fuzzy Theory and Its Applications (iFUZZY)

作者： Tsai, Chun-Wei Liao, Yu-Hsun Chiang, Ming-Chao Chia Nan Univ Pharm & Sci Dept Appl Informat & Multimedia Tainan 71710 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan

ISBN: (纸本)9781479903863

inspired by the concept and principles of quantum computing, the classical quantum-inspired evolutionary algorithm (QEA) provides a useful way to find out the approximate solution of many optimization problems. However, compared with other heuristic algorithms, the slow convergence speed of QEA has been an important issue when it is applied to solve the optimization problems. As such, an improved version, called fast quantum-inspired evolutionary algorithm (FQEA), is proposed in this paper. By adding a fast repair facility, the proposed algorithm can not only accelerate the convergence speed of the search process of QEA, it can also provide a better result than QEA. Experimental results show that the proposed algorithm FQEA can provide a better result than those obtained by QEA and k-means algorithm.

关键词： evolutionary algorithm quantum-inspired evolutionary algorithm clustering

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Fake-Face Image Classification using Improved quantum-inspired evolutionary-based Feature Selection Method

Fake-Face Image Classification using Improved Quantum-Inspir...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Mittal, Himanshu Saraswat, Mukesh Bansal, Jagdish Chand Nagar, Atulya Jaypee Inst Informat Technol Noida India South Asian Univ New Delhi India Liverpool Hope Univ Liverpool Merseyside England

ISBN: (纸本)9781728125473

Deep learning models have been quite successful in discriminating synthesized or edited fake-face images. However, in the case of small training data, transfer-learning is rather preferable. This is a complex process for high dimensional feature space due to the curse of dimensionality. To mitigate the same, this paper proposes a new feature selection method for the classification of manually created fake-face images. In the proposed method, a pre-trained deep learning model is used to extract features of an image. Next, an optimal feature subset is selected from the extracted features through an improved quantum-inspired evolutionary algorithm. Lastly, the elicited features are considered to perform the classification. Experiments are conducted on a publicly available manually created fake-face image dataset, namely Real and Fake Face Detection by Yonsei University. The performance of the proposed method is compared with two methods in terms of classification accuracy and the number of selected features. The experimental comparisons exhibit that the proposed method achieves promising results among the considered methods.

关键词： Deep Learning Fake-Face Image Classification Feature Selection quantum-inspired evolutionary algorithm

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An quantum-inspired evolutionary algorithm applied to design optimizations of electromagnetic devices

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2012年第1-4期39卷 89-95页

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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A Comprehensive Learning quantum-inspired evolutionary algorithm

A Comprehensive Learning Quantum-Inspired Evolutionary Algor...

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International Conference on Information and Business Intelligence (IBI 2011)

作者： Qin, Yanhui Zhang, Gexiang Li, Yuquan Zhang, Huishen SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

ISBN: (纸本)9783642290862

This paper proposes a Comprehensive Learning quantum-inspired evolutionary algorithm (CLQEA) by introducing the philosophy of comprehensive learning into quantum-inspired evolutionary algorithms. In CLQEA, each individual in a population learns not only from its own best historical solution searched, but also from the best solutions that other individuals found. This idea is very helpful to enhance population diversity through applying a group of elite individuals to perform Q-gates to produce offspring. Extensive experiments carried out on knapsack problems with various items show that CLQEA outperforms several QIEAs recently reported in the literature.

关键词： Comprehensive Learning quantum-inspired evolutionary algorithm knapsack problem Combinatorial Optimization

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