Browse > Article
http://dx.doi.org/10.3745/JIPS.04.0168

PSA: A Photon Search Algorithm  

Liu, Yongli (School of Computer Science and Technology, Henan Polytechnic University)
Li, Renjie (School of Computer Science and Technology, Henan Polytechnic University)
Publication Information
Journal of Information Processing Systems / v.16, no.2, 2020 , pp. 478-493 More about this Journal
Abstract
We designed a new meta-heuristic algorithm named Photon Search Algorithm (PSA) in this paper, which is motivated by photon properties in the field of physics. The physical knowledge involved in this paper includes three main concepts: Principle of Constancy of Light Velocity, Uncertainty Principle and Pauli Exclusion Principle. Based on these physical knowledges, we developed mathematical formulations and models of the proposed algorithm. Moreover, in order to confirm the convergence capability of the algorithm proposed, we compared it with 7 unimodal benchmark functions and 23 multimodal benchmark functions. Experimental results indicate that PSA has better global convergence and higher searching efficiency. Although the performance of the algorithm in solving the optimal solution of certain functions is slightly inferior to that of the existing heuristic algorithm, it is better than the existing algorithm in solving most functions. On balance, PSA has relatively better convergence performance than the existing metaheuristic algorithms.
Keywords
Evolutionary Algorithm; Genetic Algorithm; Meta Heuristic; Physical Properties; Photon Search;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 R. A. Formato, "Central force optimization: a new deterministic gradient-like optimization metaheuristic," Opsearch, vol. 46, no. 1, pp. 25-51, 2009.   DOI
2 A. Hatamlou, "Black hole: a new heuristic optimization approach for data clustering," Information Sciences, vol. 222, pp. 175-184, 2013.   DOI
3 H. Huang, M. Zhu, and J. Wang, "An improved artificial bee colony algorithm based on special division and intellective search," Journal of Information Processing Systems, vol. 15, no. 2, pp. 433-439, 2019.   DOI
4 L. Zhao and Y. Long, "An improved PSO algorithm for the classification of multiple power quality disturbances," Journal of Information Processing Systems, vol. 15, no. 1, pp. 116-126, 2019.   DOI
5 X. Song, M. Zhao, Q. Yan, and S. Xing, "A high-efficiency adaptive artificial bee colony algorithm using two strategies for continuous optimization," Swarm and Evolutionary Computation, vol. 50, article no. 100549, 2019.   DOI
6 E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009.   DOI
7 M. R. Chen, J. H. Chen, G. Q. Zeng, K. D. Lu, and X. F. Jiang, "An improved artificial bee colony algorithm combined with extremal optimization and Boltzmann Selection probability," Swarm and Evolutionary Computation, vol. 49, pp. 158-177, 2019.   DOI
8 M. Li, D. Lei, and J. Cai, "Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives," Swarm and Evolutionary Computation, vol. 49, pp. 34-43, 2019.   DOI
9 H. Kragh, "Max Planck: the reluctant revolutionary," Physics World, vol. 13, no. 12, pp. 31-36, 2000.   DOI
10 A. Einstein, "Uber einen die erzeugung und verwandlung des lichtes betreffenden heuristischen gesichtspunkt," Annalen der Physik, vol. 322, no. 6, pp. 132-148, 1905.   DOI
11 L. V. de Broglie, "On the theory of quanta," Annales de Physique, vol. 10, no. 3, pp. 22-128, 1925.   DOI
12 A. Mucherino and O. Seref, "Monkey search: a novel metaheuristic search for global optimization," AIP Conference Proceedings, vol. 953, no. 1, pp. 162-173, 2007.
13 J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942-1948.
14 D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.   DOI
15 S. Mirjalili and A. Lewis, "The whale optimization algorithm," Advances in Engineering Software, vol. 95, pp. 51-67, 2016.   DOI
16 M. Dorigo, M. Birattari, and T. Stutzle, "Ant colony optimization," IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, 2006.   DOI
17 P. C. Pinto, T. A. Runkler, and J. M. Sousa, "Wasp swarm algorithm for dynamic MAX-SAT problems," in Adaptive and Natural Computing Algorithms. Heidelberg: Springer, 2007, pp. 350-357.
18 A. Sharma, A. Sharma, B. K. Panigrahi, D. Kiran, and R. Kumar, "Ageist spider monkey optimization algorithm," Swarm and Evolutionary Computation, vol. 28, pp. 58-77, 2016.   DOI
19 Q. Zhou and Y. Q. Zhou, "Wolf colony search algorithm based on leader strategy," Application Research of Computers, vol. 30, no. 9, pp. 2629-2632, 2013.   DOI
20 C. R. Hwang, "Simulated Annealing: Theory and Applications by P. J. M. Van Laarhoven and E. H. Aarts, 1987," Acta Applicandae Mathematica, vol. 12, pp. 108-111, 1988.   DOI
21 A. Kaveh and S. Talatahari, "A novel heuristic optimization method: charged system search," Acta Mechanica, vol. 213, no. 3-4, pp. 267-289, 2010.   DOI
22 H. Shah-Hosseini, "Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimization," International Journal of Computational Science and Engineering, vol. 6, no. 1-2, pp. 132-140, 2011.   DOI