Quantum-Inspired Evolution Algorithm: Experimental Analysis
Quantum computing mimics behaviour of atoms in processing information. Unfortunately due to restrictive rules of processing imposed by quantum behaviour only few successful algorithms have been developed in quantum computing. Quantum inspired algorithm is a concept, which employs certain elements of quantum computing to use in a wider class of search and optimisation problems. The main parts of a quantum‐inspired algorithm are the qubits (quantum equivalent of bits) and the gates. Qubits hold the information in a superposition of all the states, while the quantum gates evolve the qubit to achieve the desired objective, which is, in optimization the maximum or the minimum. The paper addresses the ability of the Quantum‐Inspired Evolution Algorithm (QIEA) to solve practical engineering problems. QIEA, which is developed by authors, is based on their previous work and it is improved to test a series of unitary gates. A set of experiments were carried out to investigate the performance of QIEA as for speed, accuracy, robustness, simplicity, generality, and innovation. To assess effectiveness of a new algorithm, there are a set of guidelines proposed by . Based on these guidelines, the paper selected three test functions to carry out a benchmark study. The paper also presents a comparative study between QIEA and classical Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) techniques in order to assess the proposed QIEA.
KeywordsGenetic Algorithm Particle Swarm Optimization Quantum Computing Quantum Algorithm Quantum Gate
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- 3.Alfares, F. and Esat, I. I. (2003). Quantum Algorithms; How Useful for Engineering Problems. in Proc. of the Seventh World Conference on Integrated Design & Process Technology. Austin, Texas, USA. 669–673.Google Scholar
- 6.Moore, M. P. and Narayanan, A., (1995) Quantum-Inspired Computing. Department of Computer Science, University of Exeter, Technical Report No. 344; http://www.dcs.exeter.ac.uk.Google Scholar
- 9.Zhang, G., Jin, W. and Li, N., (2003) An Improved Quantum Genetic Algorithm and Its Application. LNAI. 2639. 449–452.Google Scholar
- 10.Narayanan, A. and Moore, M. (1996). Quantum-inspired genetic algorithms. in Proc. of the 1996 IEEE Conference on Evolutionary Computation (ICEC’ 96). Nayoya University, Japan: IEEE, 61–66.Google Scholar
- 11.Rylander, B., Soule, T., Foster, J. and Alves-Fos, J. (2000). Quantum Evolutionary Programming. in Proc. of the Genetic and Evolutionary Computation Conference (GECCO-2000), 373–379.Google Scholar
- 12.Li, B. and Zhuang, Z.-Q., (2002) Genetic algorithm based-on the quantum probability representation. LNCS. 2412. 500–505.Google Scholar
- 14.Grover, L. K. (1998). Framework for fast quantum mechanical algorithms. in Conference Proceedings of the Annual ACM Symposium on Theory of Computing, 53–62.Google Scholar
- 16.Grover, L. K. (1999). Quantum Mechanical Searching. in Proceedings of the Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2255–2261.Google Scholar
- 19.Michalewicz, Z., (1999) Genetic Algorithms + Data Structures = Evolution Programs. 3rd, revised and extended ed, Berlin: SpringerVerlag.Google Scholar
- 20.Kennedy, J. and Eberhart, R. C. (1995). Particle swarm optimization. in Proceedings of the IEEE International Conference on Neural Networks, 1942–1948.Google Scholar
- 21.Kennedy, J., Eberhart, R. C. and Shi, Y., (2001) Swarm Intelligence, San Francisco: Morgan Kaufmann Publishers.Google Scholar
- 23.Levy, A., Montalvo, A., Gomez, S. and Galderon, A., (1981) Topics in Global Optimization, New York: Springer-Verlag.Google Scholar