CN105989409A - Quantum mechanism-based intelligent optimization algorithm - Google Patents
Quantum mechanism-based intelligent optimization algorithm Download PDFInfo
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- CN105989409A CN105989409A CN201510044524.5A CN201510044524A CN105989409A CN 105989409 A CN105989409 A CN 105989409A CN 201510044524 A CN201510044524 A CN 201510044524A CN 105989409 A CN105989409 A CN 105989409A
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Abstract
The present invention relates to a quantum mechanism-based intelligent optimization algorithm. The algorithm introduces a quantum mechanism in an evolution algorithm, and combines a quantum evolution algorithm with an optimization algorithm according to different actual problems, thereby keeping the diversity of populations and accelerating the convergence speed of the evolution algorithm. The combined algorithm can give play to the advantages of the two algorithms, thereby being stronger in adaptability.
Description
Technical field
The present invention relates to a kind of intelligent optimization algorithm based on quantum mechanical.
Background technology
Intelligent optimization algorithm, such as artificial neural network, genetic algorithm, simulated annealing, particle cluster algorithm and optimisation strategy thereof etc., it is developed by simulating or disclose some natural phenomena or process, its content relates to the aspects such as mathematics, physics, biological evolution, artificial intelligence, neuroscience and statistical mechanics, provides new thinking and means for solving challenge.Quantum calculation make use of in quantum theory about quantum state superposition, tangle and the characteristic such as interference, make the problem that some computation complexity in classic computer is the highest be likely to decrease its complexity by quantum parallel computation.Owing to quantum computer not yet realizes, the most how quantum calculation is utilized also to be a focus studied at traditional computer.The invention belongs to the one of intelligent optimization algorithm, the advantage utilizing quantum mechanical, improves the ability that solves and the operation efficiency of optimized algorithm further.
Relevant references:
[1] Michael A.Nielsen, Isaac L.Chuang. Quantum Computation and Quantum
Information. Cambridge University Press, 2000.
[2] Barenco A., Deutsch D., Ekert A.and Jozsa R.Conditional
Quantum dynamics and logic gates. Phys. Rev. Lett., 1995,74 (20): 4083-4088.
[3] Shor P.W. Quantum Computing. Proceedings of the International
Congress of Mathematicians, 1998:467-486.
[4] Wang Ling, Wu Hao, Tang Fang, Zheng great Zhong, Jin Yihui. hybrid quantum GA and performance evaluation thereof. control and decision-making, 2005,20 (2): 156-160.
[5] Xia Peisu. quantum calculation. Journal of Computer Research and Development, 2001,38 (10): 1153-1171.
Summary of the invention
The present invention, on the basis of intelligent optimization algorithm, introduces quantum mechanical, and intelligent optimization algorithm has been carried out some improvement, according to the difference of Solve problems, to obtain, different optimized algorithm combinations is preferably solved ability.
Quantum combinatorial optimization algorithm (Quantum Combinatorial
Optimal Algorithm, QCOA).Algorithm is divided into two stages, it is respectively adopted quantum evolutionary algorithm and intelligent optimization algorithm two different groups GroupI are optimized with GroupII, connect each other between Liang Ge colony and influence each other, the population size of GroupI Yu GroupII is respectively n, 2n, n the initial individuals of GroupII derives from GroupI, and the optimal solution in GroupII will instruct the generation of 1/4 initial individuals in GroupI simultaneously.Optimize to keep the multiformity of population by two benches, it is to avoid precocious, use different algorithms to combine with quantum evolutionary algorithm, to improve the adaptability of algorithm according to different problems.
Whole population is divided into two parts GroupI and GroupII by combinatorial optimization algorithm (QCOA), as it is shown in figure 1, GroupI population scale is n, utilizes quantum evolutionary algorithm to be optimized；GroupII population scale is 2n, utilizes intelligent optimization algorithm to be optimized.Intelligent optimization algorithm selects different optimized algorithms according to different application problems, and such as continuity problem can select particle swarm optimization algorithm.GroupI and GroupII is separate, connects each other again, and we will provide relation therebetween in algorithm flow.
Combinatorial optimization algorithm flow process is shown in Fig. 2.Algorithm is divided into two stages, and GroupI is utilized quantum evolutionary algorithm to be optimized by the first stage, and second stage uses intelligent optimization algorithm to be optimized GroupII.Two stages will be given an explaination by respectively below.
1) first stage, when t ≠ 0, algorithm to select an optimum individual from GroupII, as guidance, generates a part of initial individuals in GroupI.It is to say, release one according to current optimum individual to instruct quantum chromosomes, then around it, random scatter quantum chromosomes, as follow-on quantum population, is formulated as
Wherein PcT () is on behalf of optimum individual only, Q to tgFor instructing quantum chromosomes, a is PcT () factor of influence, b is the variance of quantum population random scatter.Normrnd (0,1) produces the random number between 0 to 1.
Use above-mentioned view mode, chromosome P=[0 0 will be obtained with probability 1
11 0], only quantum chromosomes Q=[1 1 need to be made
00 1], i.e. Q=.If P is the optimal solution in search volume, then the quantum chromosomes in population is closer to Q, and the probability obtaining optimal solution is the biggest.The value of a is the least, and quantum population is subject toImpact the biggest, when a=0,, observeAfter will obtain P with probability 1.Typically take, b[0.1,0.3].
2) with unlike general evolution algorithm, having n individuality to come from GroupI in GroupII in initial population, remaining n individuality is n the individuality that in previous generation, fitness is best.First stage GroupI population scale is n, this n individuality will be in second stage be added into GroupII, GroupII has eliminated n individuality after previous generation has evolved, and n added after having added the first stage individual, this ensure that the population scale of second stage GroupII is 2n.
3) quantum evolutionary algorithm is combined with general optimized algorithm, the result that quantum evolution calculates can increase the multiformity of population in optimized algorithm, optimized algorithm guidance to quantum evolution in turn, the convergence of quantum evolutionary computation can be accelerated, the two is combined the advantage that can play two algorithms so that the adaptability of algorithm is higher.
Claims (1)
1. an intelligent optimization algorithm based on quantum mechanical, its feature is:
Quantum mechanical is incorporated in intelligent optimization algorithm by algorithm, the advantage making full use of quantum calculation, to keep the multiformity of population；Select intelligent optimization algorithm and quantum mechanical algorithm combination in conjunction with practical problem, to play the advantage of optimized algorithm, accelerate quantum mechanical convergence of algorithm, the two is combined the advantage that can play two algorithms so that the adaptability of algorithm is higher.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108320027A (en) * | 2017-12-29 | 2018-07-24 | 国网河南省电力公司信息通信公司 | Big data processing method based on quantum computation |
| CN108701262A (en) * | 2015-12-30 | 2018-10-23 | 谷歌有限责任公司 | Enhance simulated annealing using Quantum annealing |
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2015
- 2015-01-29 CN CN201510044524.5A patent/CN105989409A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108701262A (en) * | 2015-12-30 | 2018-10-23 | 谷歌有限责任公司 | Enhance simulated annealing using Quantum annealing |
| CN108701262B (en) * | 2015-12-30 | 2022-06-21 | 谷歌有限责任公司 | Enhancing simulated annealing using quantum annealing |
| US11900214B2 (en) | 2015-12-30 | 2024-02-13 | Google Llc | Enhancing simulated annealing with quantum annealing |
| CN108320027A (en) * | 2017-12-29 | 2018-07-24 | 国网河南省电力公司信息通信公司 | Big data processing method based on quantum computation |
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Application publication date: 20161005 |