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multi-scale quantum harmonic oscillator algorithm With Centroid Motion for Global Numerical Optimization Problems

IEEE ACCESS 2019年 7卷 90055-90068页

作者： Ye, Xinggui Wang, Peng Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Sichuan Peoples R China China Unicorn Fujian Branch Cloud Comp & Big Data Ctr Fuzhou 350007 Fujian Peoples R China Southwest Minzu Univ Sch Comp Sci & Technol Chengdu 610225 Sichuan Peoples R China

This paper proposes a new approach for function optimization using a new variant of multi-scale quantum harmonic optimization algorithm (MQHOA). The new approach introduces a centroid motion to improve the convergence efficiency, which is called MQHOA with centroid motion (CM-MQHOA). Instead of replacing the worst particle by the current best individual in the quantum harmonic oscillator process in MQHOA, the weakest player is replaced by a current centroid position in the proposed algorithm. Simple mechanisms are added to maintain the diversity of the population and help achieve the global optima in difficult unimodal and multimodal search spaces. The benefits of the proposed algorithm are improved performance in terms of effectiveness, reliability, accuracy, and efficiency. The approach appears to be able to efficiently deal with several unimodal and multimodal benchmark functions. A variety of standard benchmark functions are used to illustrate the proposed approach. The experimental results are compared with several state-of-the-art optimization algorithms. The comparative results indicate the competitiveness of the proposed algorithm and suggest a viable and attractive addition to the portfolio of computational intelligence techniques.

关键词： multi-scale quantum harmonic oscillator algorithm heuristic algorithm population-based algorithm global optimization centroid motion

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multi-scale quantum harmonic oscillator algorithm With Truncated Mean Stabilization Strategy for Global Numerical Optimization Problems

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IEEE ACCESS 2019年 7卷 18926-18939页

作者： Ye, Xinggui Wang, Peng Xin, Gang Jin, Jin Huang, Yan Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Sichuan Peoples R China China Unicom Cloud Comp & Big Data Ctr Fujian Branch Fuzhou 350007 Fujian Peoples R China Southwest Minzu Univ Sch Comp Sci & Technol Chengdu 610225 Sichuan Peoples R China Huaiyin Normal Univ Sch Comp Sci & Technol Huaian 223300 Peoples R China

A multi-scale quantum harmonic oscillator algorithm (MQHOA) is a quantum population-based algorithm proposed recently. It utilizes the quantum wave function to locate the global optimum of a global numerical optimization problem. As the MQHOA employs the elitism to replace the worst particle in each iteration cycle, it reduces one of the particles in each run, which will cripple the diversity of the population and slow down the convergence speed. Therefore, the particles will be easily trapped into local optima. In this paper, we suggest a new MQHOA with truncated mean stabilization (TS-MQHOA) policy to alleviate the above-mentioned problems. The theoretical and experimental analyses indicate that the truncated mean stabilization strategy helps to diversify the populations and improve the convergence efficiency. The proposed TS-MQHOA is evaluated on a number of dimensionwise unimodal and multimodal CEC benchmark functions, and the computational results are compared with several popular population-based algorithms. The experimental results on complex test problems demonstrate that the proposed TS-MQHOA, in most function evaluations, is able to obtain better convergence toward the global optimum compared with several renowned heuristic algorithms based on swarm intelligence. Meanwhile, the comparative results reveal the competitiveness and superiority of the proposed algorithm, especially on high-dimensional function evaluations.

关键词： multi-scale quantum harmonic oscillator algorithm swarm intelligence population-based algorithm stochastic algorithm truncated mean stabilization strategy

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A Novel Dynamic Analysis on multi-scale quantum harmonic oscillator algorithm Using Double-well Function 22

A Novel Dynamic Analysis on Multi-scale Quantum Harmonic Osc...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Yang, Guosong Wang, Peng Yin, Xinyu Chinese Acad Sci Chengdu Inst Comp Applicat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Southwest Minzu Univ Key Lab Comp Syst State Ethn Affairs Commiss Chengdu Peoples R China

ISBN: (纸本)9781450392686

Based on the double-well function, a novel dynamic analysis is proposed to study the optimization mechanism of the multi-scale quantum harmonic oscillator algorithm. This procedure-oriented method gives new clues for the algorithm's improvement, it can be adapted to study other algorithms' evolution process and complement the prevailing performance-oriented benchmark testing.

关键词： Optimization mechanism swarm evolution mechanism doublewell function multi-scale quantum harmonic oscillator algorithm

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Adaptive multi-scale quantum harmonic oscillator algorithm Based on Evolutionary Strategy

Adaptive Multi-scale Quantum Harmonic Oscillator Algorithm B...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ye, Xinggui Wang, Peng Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu Peoples R China Southwest Minzu Univ Sch Comp Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9781728169293

This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical optimization. Since the original multi-scale quantum harmonic oscillator algorithm (MQHOA) utilizes a fixed contraction factor to narrow the search scale, the searching step decreases too fast at the later stage of the evolution and is more likely to suffer premature convergence and stagnation. To improve the convergence performance, an adaptive attenuation mechanism of scaling is proposed to dynamically adjust the exploration and exploitation properties. Evolutionary strategies such as selection, crossover and DE/rand/1 mutation are implemented in the proposed algorithm to enhance the exploration and exploitation abilities. Experimental results evaluated on several unimodal and multimodal benchmark functions indicate the significant improvement of the proposed algorithm to the original MQHOA. Meanwhile, the experimental results compared with several state-of-the-art optimizers show the superiority or competitiveness of the proposed algorithm.

关键词： Evolutionary strategy differential evolution adaptive mechanism multi-scale quantum harmonic oscillator algorithm population-based optimization

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Impact of migration strategies and individual stabilization on multi-scale quantum harmonic oscillator algorithm for global numerical optimization problems

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APPLIED SOFT COMPUTING 2019年 85卷 105800-000页

作者： Ye, Xinggui Wang, Peng Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Sichuan Peoples R China Southwest Minzu Univ Sch Comp Sci & Technol Chengdu 610225 Sichuan Peoples R China China Unicom Fujian Branch Cloud Comp & Big Data Ctr Fuzhou 350007 Fujian Peoples R China

multi-scale quantum harmonic oscillator algorithm (MQHOA) is a population-based metaheuristic algorithm proposed recently. It has been proved effective and efficient to deal with unimodal and multimodal problems. Although the mechanism of replacing the worst particle with the fittest individual in MQHOA helps to fasten the iteration process, it can easily lead to premature convergence. Instead of direct replacement, several migration strategies are proposed to maintain the diversity of the population and help to obtain the global optima in difficult function evaluations. The impacts of the migration strategies and individual stabilization on the improvement of the algorithms in their effectiveness, reliability, accuracy and efficiency are well researched. A variety of multi-dimensional unimodal and multimodal benchmark functions are applied to illustrate the optimization performance of the proposed algorithms. Some of the best competitors in MQHOAs with migration strategies are selected to compare with several state-of-the-art stochastic algorithms. Experimental results presented suggest some conclusions: First, the individual stabilization mechanism does not significantly improve the performance of MQHOA. Second, random migration does not obviously help MQHOA perform much better. Third, migration strategies significantly affect the performance of MQHOA, and some of MQHOAs with migration strategies are very competitive to deal with numerical optimization problems. (C) 2019 Published by Elsevier B.V.

关键词： multi-scale quantum harmonic oscillator algorithm Population-based algorithm Stochastic algorithm Migration strategy Individual stabilization

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