A region-based quantum evolutionary algorithm (RQEA) for global numerical optimization

Lu, Tzyy-Chyang; Juang, Jyh‐Ching

doi:10.1016/j.cam.2012.09.015

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…Firstly, a set of traditional, basic functions, was taken from the first 13 functions presented in [8]. Additionally, a non-transformed basic version of Schwefel 7 [25] was used when comparing to data published for three recent QIEA [37,42,43], and a basic two dimensional problem from [44], when comparing another QIEA. A second set of more complicated functions was added from the first 20 functions defined in the CEC-2013 specification [9].…”

Section: Test Functionsmentioning

confidence: 99%

“…A comparison of SRQEA with five different QIEAs is given in Table 8: a hybrid quantum PSO algorithm HRCQEA [37], a region based QIEA RQEA [42], a hybrid quantum PSO with neighbourhood search NQPSO [43], and two hybrid quantum GAs QGAXM [54] and CQGA [44]. The five fitness functions used in [37] where available in [42] and [43], so were chosen for comparison. When comparing to QGAXM and CQGA, the evaluated fitness functions were matched in their entirety, including a two-dimensional…”

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confidence: 99%

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Convergence properties of quantum evolutionary algorithms on high dimension problems

Wright

Jordanov

2019

Neurocomputing

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We propose and investigate new rotation gates for two modified Quantum Inspired Evolutionary methods for solving high dimension optimisation problems. The Quantum Inspired Evolutionary Algorithms (QIEA) were originally used for solving binary encoded problems and their signature features follow superposition of multiple states on a quantum bit and a rotation gate. In order to apply this paradigm to high dimension problems, we propose two quantum methods Half Significant Bit (HSB) and Stepwise Real QEA (SRQEA), developed using binary and real encoding respectively, while keeping close to the original quantum computing metaphor. We introduce five performance metrics and use them to evaluate the proposed approaches against sets of multimodal mathematical test functions and real world problems of high dimensionality. We report issues found while implementing some of the published real QIEA techniques which were the motivation for developing our real algorithm modifications. Our methods focus on introducing and implementing new rotation gate operators used for evolution, including a novel mechanism for preventing premature convergence in the binary algorithm. The applied performance metrics show superior results for our quantum methods on most of the test problems (particularly with high dimension problems), demonstrating faster convergence and accuracy.

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Section: Test Functionsmentioning

confidence: 99%

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confidence: 99%

Convergence properties of quantum evolutionary algorithms on high dimension problems

Wright

Jordanov

2019

Neurocomputing

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“…To overcome these obstacles and obtain better solutions, this paper introduces quantum representation and quantum rotation gate in the quantum evolutionary algorithm (QEA) to improve the PIO algorithm. The QEA is a probabilistic evolutionary algorithm that integrates concepts from quantum computing for robust search [22]. The QEA uses a qubit as the probabilistic representation, which represents a linear superposition of binary solutions [23].…”

Section: Introductionmentioning

confidence: 99%

Minimum-Fuel Ascent of Hypersonic Vehicle considering Control Constraint Using the Improved Pigeon-Inspired Optimization Algorithm

Liu

et al. 2020

International Journal of Aerospace Engineering

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Trajectory optimization problem for hypersonic vehicles has long been recognized as a difficult problem. This paper brings control constraints into the trajectory optimization to make the optimal trajectory meet the requirements of control performance. The strong nonlinear characteristic of the ascent phase aerodynamics makes the trajectory optimization problem difficult to be solved by the optimal control theory. A trajectory optimization algorithm based on the improved pigeon-inspired optimization (PIO) algorithm is proposed to solve the complex trajectory optimization problem under multiple constraints. To overcome the obstacle of premature convergence and deceptiveness, the evolutionary strategy of qubit in quantum evolutionary algorithm (QEA) is introduced into the PIO to maintain population diversity and judge the optimal solution. To handle constraints, the penalty function is used to construct the fitness function. The optimal ascent trajectory is obtained by utilizing the improved PIO algorithm. Then, the trajectory inverse algorithm is used to verify the feasibility of the optimal trajectory to ensure that a feasible optimal trajectory is obtained. The comparison results show that the proposed algorithm outperforms particle swarm optimization (PSO) and standard PIO on trajectory optimization. Meanwhile, the simulation result shows that the performance of the optimal ascent trajectory with control constraints is improved and the trajectory is feasible. Therefore, the method is potentially feasible for solving the ascent trajectory optimization problem under control constraint for hypersonic vehicles.

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“…The quantum evolutionary algorithm (QEA) is a probabilistic evolutionary algorithm that integrates concepts from quantum computing for robust search [16]. QEA uses a qubit as the probabilistic representation, which represents a linear superposition of binary solutions.…”

Section: Introductionmentioning

confidence: 99%

A hybrid quantum-based PIO algorithm for global numerical optimization

Chen¹,

Hao²,

Shen³

et al. 2019

Sci. China Inf. Sci.

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Hybrid quantum particle swarm optimization algorithm and its application SCIENCE CHINA Information Sciences 63, 159201 (2020); An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization SCIENCE CHINA Information Sciences 53, 980 (2010); A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus SCIENCE CHINA Technological Sciences 57, 2542 (2014); Multi-mode energy management strategy for hydraulic hub-motor auxiliary system based on improved global optimization algorithm SCIENCE CHINA Technological Sciences SCIENCE CHINA Information Sciences

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A region-based quantum evolutionary algorithm (RQEA) for global numerical optimization

Cited by 12 publications

References 30 publications

Convergence properties of quantum evolutionary algorithms on high dimension problems

Convergence properties of quantum evolutionary algorithms on high dimension problems

Minimum-Fuel Ascent of Hypersonic Vehicle considering Control Constraint Using the Improved Pigeon-Inspired Optimization Algorithm

A hybrid quantum-based PIO algorithm for global numerical optimization

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