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A new real-coded quantum-inspired evolutionary algorithm for continuous optimization

APPLIED SOFT COMPUTING 2017年 61卷 765-791页

作者： Talbi, Hichem Draa, Amer Constantine 2 Univ NTIC Fac MISC Lab Constantine Algeria Constantine 2 Univ MISC Lab Constantine Algeria

This paper presents a recursive deepening hybrid strategy to solve real-parameter optimization problems. It couples a local search technique with a quantum-inspired evolutionary algorithm. In order to adapt the quantum-inspired evolutionary algorithm for continuous optimization without losing the states super-position property, a suitable sampling of the search space that tightens recursively and an integration of a uniformly generated random part after measurement have been utilized. The use of local search provides, for each search window, a good exploitation of the quantum inspired generated solution's neighbourhood. The proposed approach has been tested through the reference black-box optimization benchmarking framework. The comparison of the obtained results with those of some state-of-the-art algorithms has shown its actual effectiveness. (C) 2017 Elsevier B.V. All rights reserved.

关键词： quantum-inspired evolutionary algorithms Local search Continuous optimization Exploration Exploitation

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Scalability Property in Solving the Density Classification Task

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JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2017年第2期10卷 60-76页

作者： Zakaria, Laboudi Salim, Chikhi Saliha, Lakhdari Constantine2 Univ MISC Lab Constantine Algeria Univ Oum El Bouaghi RELA CS Lab 2 Oum El Bouaghi Algeria

The density classification task (DCT for short) is one of the most studied benchmark problems for analyzing emergent computations performed by cellular automata. Starting from the observation that the performance of the best known solutions is not stable towards initial configurations size;we propose in this paper, some new evolutionary mechanisms for designing new solutions with similar conceptual properties to the best known ones. The approach is based on varying the size of initial configurations which allows making comparisons and analysis between the different solutions. We show then through a set of numerical results that the proposed mechanism allows collecting solutions for the DCT more efficiently and with reduced efforts. Also, we show that the collected solutions are affected by configurations size variations, where only few of them are scalable.

关键词： Cellular Automata Density Classification Task evolutionary algorithms Genetic algorithms quantum-inspired evolutionary algorithms State-Conserving Property Symmetry Property

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quantum-inspired optimization of echo state networks applied to system identification

Quantum-inspired optimization of echo state networks applied...

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IEEE Congress on evolutionary Computation

作者： Paulo R. M. Paiva Marley M. B. R. Vellasco José F. M. Amaral Electrical Engineering Department Pontifical Catholic University of Rio de Janeiro Rio de Janeiro - RJ Brazil Department of Electronics and Telecommunication Engineering Rio de Janeiro State University Rio de Janeiro - RJ Brazil

quantum-inspired evolutionary algorithms (QIEA) represent an efficient alternative to the traditional genetic algorithms, being capable of finding good solutions with smaller populations. Echo State Networks (ESNs) are a simple and efficient implementation of the Reservoir Computing framework. The use of this kind of networks in system identification is advantageous due to its intrinsic dynamic behavior and fast training procedure. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, the random generation of the reservoir weights of these networks may not be ideal in terms of performance. Thus, this work presents a method that automatically defines an ESN for system identification problems by using a real coded QIEA (QIEA-R) in a two-phase optimization procedure. The QIEA-R firstly searches for the best global parameters of an ESN;then, on a second stage, optimizes some of its reservoir weights. In two benchmark problems for system identification, the proposed method overcame the performance of a randomly generated ESN with the same global parameters and has presented comparable and, in most cases, better accuracy results in comparison to some methods which were applied to the same datasets.

关键词： Echo State Networks quantum-inspired evolutionary algorithms System Identification

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Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2016年第9期47卷 2225-2236页

作者： Yu, Gwo-Ruey Huang, Yu-Chia Cheng, Chih-Yung Natl Chung Cheng Univ Dept Elect Engn Chiayi 621 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi 621 Taiwan Natl Taiwan Ocean Univ Dept Elect Engn Keelung Taiwan

In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi-Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.

关键词： polynomial fuzzy control Takagi-Sugeno fuzzy control quantum-inspired evolutionary algorithms Lyapunov function sum of squares

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FP-AK-QIEA-R for Multi-Objective Optimization 16

FP-AK-QIEA-R for Multi-Objective Optimization

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3rd International Conference on Biomedical and Bioinformatics Engineering (ICBBE)

作者： Chire Saire, Josimar Edinson Univ Catolica San Pablo Arequipa Peru

ISBN: (纸本)9781450348249

The evolutionary algorithms have main features like: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The FP-AK-QIEA-R uses probability density function according to best of initial population to sample new population and uses rewarding criteria to sample around the best of every iteration using cumulative density function estimated for Akima interpolation, it was used for mono-objective problems showing good results. The proposal uses the algorithm FP-AK-QIEA-R and add Pareto dominance to experiment with multiobjective problems. The performed experiments use some benchmark functions from the literature and initial results shows a promissory way for the algorithm.

关键词： quantum-inspired evolutionary algorithms evolutionary algorithms Particle filters Akima interpolation pdf estimation Multi-objective optimization

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quantum-inspired evolutionary Algorithm for Topology Optimization of Modular Cabled-Trusses

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2015年第8期22卷 670-680页

作者： Finotto, V. C. Lucena, D. S. da Silva, W. R. Leal Valasek, M. Czech Tech Univ Dept Mech Biomech & Mechatron Fac Mech Engn Prague 16629 Czech Republic Pontificia Univ Catolica Parana Fac Mech Engn Dept Mechatron Curitiba Parana Brazil Czech Tech Univ Fac Civil Engn Dept Mech Prague 16629 Czech Republic

This article proposes an alternative optimization framework applied to topology optimization of modular lightweight cabled-truss structures. These structures are described as a system of intrinsically positioned cables and triangular bar formations jointed at their ends by hinged connections to form a rigid framework. The optimized topologies are determined through a stochastic discrete optimization procedure that uses ground structure approach, nonlinear finite element analysis, and quantum-inspired evolutionary algorithms. The optimization searches for optimal mass reduction with minimal losses in stiffness, such relation, is expressed by the stiffness-to-mass ratio parameter. Nonlinear finite element analysis is used to evaluate the static structural response. In order to decrease computation time, kinematically instable and structurally invalid individuals are filtered before evaluation. Modular design approach is taken into consideration to reduce the number of design variables and increase the productibility of cabled-trusses. Symmetric structural response is desired since in several mechanical applications forces can assume different directions during the working cycle. A modular ground structure with 300 elements is optimized, and optimal truss and cabled-truss topologies are compared. Complementary analyses comprise the investigation of the structural performance under different number of modules and slenderness ratios. The results indicate that the proposed optimization framework leads to optimized structures. In addition, it was observed that cabled trusses presented significant improvements in structural performance when compared with trusses.

关键词： lightweight structures topology optimization cabled-truss pre-stressed structures quantum-inspired evolutionary algorithms

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evolutionary Algorithm-based Parameter Identification for Nonlinear Dynamical Systems

Evolutionary Algorithm-based Parameter Identification for No...

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IEEE Congress on evolutionary Computation (CEC)

作者： Banerjee, Amit Abu-Mahfouz, Issam Penn State Univ Harrisburg Sch Sci Engn & Technol Middletown PA 17057 USA

ISBN: (纸本)9781424478354

The inverse problem of parameter estimation for Duffing oscillator, a chaotic dynamical system well known in engineering is solved using quantum-inspired evolutionary algorithm, differential evolution and genetic algorithms. The paper focuses on such combination of parameters that produce periodic responses instead of purely chaotic responses. The feature set used is a set of displacement values of the first five Poincare points, after ignoring transient effects. All approaches correctly identify the target set of parameters as producing the given response;however, depending on the fitness landscape some parameters are more difficult to identify than others especially when using the canonical genetic algorithm. This paper is also the first to investigate the quantum-inspired evolutionary algorithm for such parameter identification problems.

关键词： Duffing oscillator parameter identification chaotic systems quantum-inspired evolutionary algorithms

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quantum-inspired Differential Evolution on Bloch Coordinates of Qubits

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International Conference on Advances in Computing, Communication and Control (ICAC3)

作者： Pat, Ankit Hota, Ashish Ranjan Singh, Avneet Indian Inst Technol Dept Math Kharagpur 721302 W Bengal India Indian Inst Technol Dept Elect Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9783642184390

Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces. On the other hand, a variation of the original quantum-inspired evolutionary algorithm (QEA), bloch quantum-inspired evolutionary algorithm (BQEA), is a promising concept which very well suitable for handling global optimization problem of low dimensionality. BQEA applies several quantum computing techniques such as qubit representation based on bloch sphere and rotation gate operator, etc. This paper extends the concept of differential operators to the quantum paradigm and proposes the bloch quantum-inspired differential evolution algorithm (BQDE). The performance of BQDE is found to be significantly superior as compared to BQEA on several benchmark functions.

关键词： Differential Evolution Bloch coordinates quantum-inspired evolutionary algorithms quantum Computing

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evolutionary Multi-objective Optimization algorithms with Probabilistic Representation Based on Pheromone Trails

Evolutionary Multi-objective Optimization Algorithms with Pr...

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2010 IEEE World Congress on Computational Intelligence

作者： Li, Hui Landa-silva, Dario Gandibleux, Xavier Univ Nottingham Sch Comp Sci Automated Scheduling Optimisat & Planning ASAP Re Nottingham NG7 2RD England Univ Nantes Dept Comp Sci Lab Informat F-44035 Nantes France

ISBN: (纸本)9781424481262

Recently, the research on quantum-inspired evolutionary algorithms (QEA) has attracted some attention in the area of evolutionary computation. QEA use a probabilistic representation, called Q-bit, to encode individuals in population. Unlike standard evolutionary algorithms, each Q-bit individual is a probability model, which can represent multiple solutions. Since probability models store global statistical information of good solutions found previously in the search, QEA have good potential to deal with hard optimization problems with many local optimal solutions. So far, not much work has been done on evolutionary multi-objective (EMO) algorithms with probabilistic representation. In this paper, we investigate the performance of two state-of-the-art EMO algorithms MOEA/D and NSGA-II, with probabilistic representation based on pheromone trails, on the multi-objective travelling salesman problem. Our experimental results show that MOEA/D and NSGA-II with probabilistic presentation are very promising in sampling high-quality offspring solutions and in diversifying the search along the Pareto fronts.

关键词： Pareto fronts Q-bit evolutionary computation evolutionary computation evolutionary multiobjective optimization algorithms global statistical information multiobjective travelling salesman problem pheromone trails probabilistic representation quantum-inspired evolutionary algorithms travelling salesman problems

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quantum-inspired evolutionary algorithms and Binary Particle Swarm Optimization for training MLP and SRN neural networks

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2005年第4期2卷 561-568页

作者： Venayagamoorthy, GK Singhal, G Univ Missouri Real Time Power & Intelligent Syst Lab Dept Elect & Comp Engn Rolla MO 65409 USA

This paper presents a comparison of two machine learning methods inspired by nano-scale and macro-scale natural processes and related to distributed intelligence, namely quantum-inspired evolutionary Algorithm (QEA) and Binary Particle Swarm Optimization (BPSO). QEA is based on the concepts and principles of quantum Computing, such as a quantum bit (Q-bit) and superposition of states. QEA uses a Q-bit for the probabilistic representation and a Q-bit individual as a string of Q-bits. A modified QEA with less memory requirements is also presented. The effectiveness of these algorithms in binary search space are compared for training neural networks. Results are presented for Multilayer Perceptrons (MLPs) and Simultaneous Recurrent Neural Networks (SRNs). For neural networks trained on complex nonlinear functions, the QEA based algorithms achieve convergence faster than BPSO.

关键词： quantum-inspired evolutionary algorithms Particle Swarm Optimization Multilayer Perceptron Simultaneous Recurrent Neural Network binary training algorithms

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