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1542 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 Table 4 shows the optimization results of BIQACA algorithm when D 46 , DJ 8 , B 70 and max = max = min = PL max = 0.001 were constrained; Figure 6 shows the cost convergence curves of multicast tree for three algorithms (GA, QCMR-ACS, BIQACA) Route request s=1 M=[2, 4, 5, 7] Figure 6. TABLE IV. BIQACA ALGORITHM OPTIMIZATION RESULTS Optimal multicast tree (1, 2), (1, 3), (3, 4), (3, 5) (4, 6), (6, 7) Delay Delay jitter Packet loss rate Cost 45 7 0.0001 66 Cost convergence curves of multicast tree for three algorithms Table 5 shows the optimization results of BIQACA algorithm when D 50 , DJ 6 , B 70 and max = max = min = PL max = 0.001 were constrained; Figure 7 shows the cost convergence curves of multicast tree for three algorithms (GA, QCMR-ACS, BIQACA) Route request s=1 M=[2, 4, 5, 7] Figure 7. TABLE V. BIQACA ALGORITHM OPTIMIZATION RESULTS Optimal Delay Packet Delay multicast tree jitter loss rate Cost (1, 2), (1, 3), (2, 4), (3, 5) 49 5 0.0002 62 (4, 6), (6, 7) Cost convergence curves of multicast tree for three algorithms It can be seen from Figure 6 and Figure 7 that, under the conditions of the two multicast routing constraints, the three algorithms can all converge to the global optimal solution, for GA algorithm in Ref. [15], evolution generations during convergence were 12 and 14, respectively, QCMR-ACS algorithm in Ref. [16] requires 6 and 9 generations respectively, while the BIQACA algorithm herein requires only 2 generations, its convergence speed is much faster than that of the GA and QCMR-ACS based QoS multicast routing algorithms, thus the feasibility and effectiveness of the BIQACA algorithm are verified. IV. CONCLUSION In this paper, by combining quantum computation and ant colony algorithm, an improved quantum ant colony algorithm based on Bloch coordinates is presented, enriching the research field of quantum intelligence algorithm. From the perspective of quantum computation, this algorithm proposes the adjustment strategy of search space in accordance with the exponential decrease method. A number of qubits at current position of ants are selected using the principle of randomness to constitute the update vector, the position update, position variation and random behavior of ants are all subject to the constraint of update vector, thus improving the convergence speed of the algorithm. At the same time, the random behavior of ants introduced can obviously overcome the prematurity of the algorithm. Different solution space transformation models and fitness functions are designed for different optimization problems, where the overall idea of the algorithm remains the same, the algorithm has strong versatility. Research results show that the new algorithm has certain practical value which can improve the efficiency and accuracy. Compared with conventional intelligence algorithms, BIQACA has stronger search capability and higher efficiency, and is appropriate for complex function optimization and combinatorial optimization problems. At the same time, as a novel optimization algorithm, BIQACA is lack of necessary theoretical proof, experimental verification alone is not comprehensive enough; further study is needed in the future. REFERENCES [1] Dorigo M, Maniezzo V, Colorni A.The Ant System: Optimization by a Colony of Cooperating A gents.IEEE Trans.on SMC, vol. 26, no. 1, pp. 28-41, 1996. [2] Zhi Jun Hu, Rong Li, Ant Colony Optimization Algorithm for the 0-1 Knapsack Problem Based on Genetic Operators, Advanced Materials Research, pp. 230-232, 2011 [3] Ch. Piao, X.Han, Y. Wu. Improved ant colony algorithm for solving assignment problem, Proceedings of International Conference on Computer Application and System Modeling, 2010 [4] L. Xing, Y. Chen, A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems, Applied Soft Computing, vol. 10, no. 3, pp. 888-896, 2010. [5] L. M. Gambardella1, R. Montemanni, An Enhanced Ant Colony System for the Sequential Ordering Problem, Proceedings of the 41st Annual Conference Italian Operational Research Society, 2010 [6] Hsioa Y. T, Computer network load-balancing and routing by ant colony optimization. Proceedings of the 12th IEEE © 2013 ACADEMY PUBLISHER
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1543 International Conference on Networks, vol. 1, pp. 313-318, 2004 [7] Q. H. Gu, S. G. Jing, Study on Vehicle Routing and Scheduling Problems in Underground Mine Based on Adaptively ACA, Applied Mechanics and Materials, vol. 157, pp. 1293-1296, 2012 [8] Gomez J. F, Khodr H. M, De Oliveira P M, et al. Ant colony system algorithm for the planning of primary distribution circuits. IEEE Trnas on Power Systems, vol. 19, no. 2, pp. 996-1004, 2004. [9] Yu Zhen Yu et al, Regulation of PID Controller Parameters Based on Ant Colony Optimization Algorithm in Bending Control System, Applied Mechanics and Materials, pp. 128-129, 205, 2011. [10] Ajit Narayanan, Mark Moore, Quantum-inspired genetic algorithms, Proceeding of IEEE International Conference on Evolutionary Computation 1996, 61-66 [11] FENG An-hui, SU Hong-sheng. Improved Quantum Genetic Algorithm and Its Application. Computer Engineering, vol. 37, no. 5, pp. 199-201, 2011. [12] Panchi Li. Quantum Ant Colony Optimization with Application. Proceeding of Sixth International Conference on Natural Computation (ICNC), vol. 37, no. 6, pp. 2989 – 2993, 2010. [13] Leung YW, Wang YP. An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans. on Evolutionary Computation, vol. 5, no. 1, pp. 41- 53, 2001. [14] Wang YP, Dang CY. An evolutionary algorithm for global optimization based on level-set evolution and Latin squares. IEEE Trans. on Evolutionary Computation, vol. 11, no. 5, pp. 579-595, 2007. [15] Wang Z. ying, Shi B. xin. Solving QoS Multicast Routing Problem Based on Heuristic Genetic Algorithm, Chinese J Computers, vol. 24, no. 1, pp. 55-61, 2001. [16] YANG Yun, XU Jia, GAO Fei, et al. Multiple QoS Constrained Multicast Routing Algorithm based on ACS. MINI- MICRO SYST EMS, vol. 27, no. 11, pp. 2030-2035, 2006 [17] Jian Zhang, Jiliu Zhou, Kun He, Huanzhou Li, “Image edge detection using quantum ant colony optimization”, International Journal of Digital Content Technology and its Applications, vol. 6, no. 11, pp. 187-195, June 2012. [18] Jiyun Bai, Shiyong Li, “Gear fault diagnosis based on relevance vector machine with quantum-inspired ant colony optimization”, Journal of Information and Computational Science, vol. 7, no. 14, pp. 3169-3175, December 2010. [19] Xiao-ming You, Sheng Liu, Yu-Ming Wang, “Quantum dynamic mechanism-based parallel ant colony optimization algorithm”, International Journal of Computational Intelligence Systems, vol. 3, no. s1, pp. 101-113, December 2010. [20] Xia Chen, Chen Tang, “Improved ant colony optimization algorithms for ground state energy of quantum mechanical systems”, Chinese Journal of Computational Physics, vol. 27, no. 4, pp. 624-632, July 2010. [21] Ling Wang, Qun Niu, Minrei Fei, “A novel quantum ant colony optimization algorithm and its application to fault diagnosis”, Transactions of the Institute of Measurement and Control, vol. 30, no. 3-4, pp. 313-329, August 2008. © 2013 ACADEMY PUBLISHER
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Journal of Computers ISSN 1796-203X
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Corn Moisture Measurement using a C
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Call for Papers and Special Issues
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(Contents Continued from Back Cover
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