Abstract
Fruit fly optimization algorithm (FOA) was a novel swarm intelligent algorithm inspired by the food finding behavior of fruit flies. Due to the deficiency of trapping into the local optimum of FOA, a new fruit fly optimization integrated with chaos operation (named CFOA) was proposed in this paper, in which logistic chaos mapping was introduced into the movement of the fruit flies, the optimum was generated by both the best fruit fly and the best fruit fly in chaos. Experiments on single-mode and multi-mode functions show CFOA not only outperforms the basic FOA and other swarm intelligence optimization algorithms in both precision and efficiency, but also has the superb searching ability.
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References
Kennedy, J., Eberhard, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, USA, pp. 1942–1948 (1995)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine 22, 52–67 (2002)
Krishnanand, K.N., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: IEEE Swarm Intelligence Symposium, Pasadena, California, USA, pp. 84–91 (2005)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department 129(2), 2865–2874 (2005)
Hadded, O.B., Afshar, A., Marino, M.A.: Honey-bees mating optimization (HBMO) algorithm. Earth and Environmental Science 20(5), 661–680 (2006)
Sun, S.Y., Li, J.W.: A two-swarm cooperative particle swarms optimization. Swarm and Evolutionary Computation 15, 1–18 (2014)
Chatzis, S.P., Koukas, S.: Numerical optimization using synergetic swarms of foraging bacterial populations. Expert Systems with Applications 38(12), 15332–15343 (2011)
Mavrovouniotis, M., Yang, S.X.: Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Applied Soft Computing 13(10), 4023–4037 (2013)
Ma, Q.Z., Lei, X.J., Zhang, Q.: Mobile Robot Path Planning with Complex Constraints Based on the Second-order Oscillating Particle Swarm Optimization Algorithm. In: 2009 World Congress on Computer Science and Information Engineering, Los Angeles, USA, vol. 5, pp. 244–248 (2009)
Lei, X.J., Fu, A.L.: Two-Dimensional Maximum Entropy Image Segmentation Method Based on Quantum-behaved Particle Swarm Optimization Algorithm. In: Proceedings of the 4rd International Conference on Natural Computation, Jinan, China, vol. 3, pp. 692–696 (2008)
Tan, Y.: Particle Swarm Optimizer Algorithms Inspired by Immunity-Clonal Mechanism and Their Application to Spam Detection. International Journal of Swarm Intelligence Research 1(1), 64–86 (2010)
Kuo, R.J., Syu, Y.J., Chen, Z.Y., Tien, F.C.: Integration of particle swarm optimization and genetic algorithm for dynamic clustering Original Research Article. Information Sciences 195, 124–140 (2012)
Lei, X.J., Tian, J.F., Ge, L., Zhang, A.D.: Clustering and Overlapping Modules Detection in PPI Network Based on IBFO. Proteomics 13(2), 278–290 (2013)
Lei, X.J., Wu, S., Ge, L., Zhang, A.D.: The Clustering Model and Algorithm of PPI Network Based on Propagating Mechanism of Artificial Bee Colony. Information Sciences 247, 21–39 (2013)
Pan, W.T.: A new evolutionary computation approach: Fruit fly optimization algorithm. In: 2011 Conference of Digital Technology and Innovation Management, Taipei (2011)
Li, H.Z., Guo, S., Li, C.J., et al.: A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge-Based Systems 37, 378–387 (2013)
Lin, S.M.: Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network. Neural Comput. Appl. 22(3-4), 783–791 (2013)
Pan, W.T.: A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems 26, 69–74 (2012)
Wang, S., Yan, B.: Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle. Nonlinear Dynamics 73(1-2), 611–619 (2013)
Zheng, X.L., Wang, L., Wang, S.Y.: A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowledge-Based Systems 57, 95–103 (2014)
Wang, L., Zheng, X.L., Wang, S.Y.: A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowledge-Based System 48, 17–23 (2013)
Pecora, L., Carroll, T.L.: Synchronization in chaotic system. Phy. Rev. Lett. 64(8), 821–824 (1990)
Zhou, Y.C., Bao, L., Chen, C.L.P.: A new 1D chaotic system for image encryption. Signal Processing 97, 172–182 (2014)
Kassem, A., Hassan, H.A.H., Harkouss, Y., et al.: Efficient neural chaotic generator for image encryption. Digital Signal Processing 25, 266–274 (2014)
Ugur, M., Cekli, S., Uzunoglu, C.P.: Amplitude and frequency detection of power system signals with chaotic distortions using independent component analysis. Electric Power Systems Research 108, 43–49 (2014)
Petrauskiene, V., Survila, A., Fedaravicius, A., et al.: Dynamic visual cryptography for optical assessment of chaotic oscillations. Optics & Laser Technology 57, 129–135 (2014)
Yang, G., Yi, J.Y.: Delayed chaotic neural network with annealing controlling for maximum clique problem. Neurocomputing 127(15), 114–123 (2014)
Wang, J.Z., Zhu, S.L., Zhao, W.G., et al.: Optimal parameters estimation and input subset for grey model based on chaotic particle swarm optimization algorithm. Expert Systems with Applications 38(7), 8151–8158 (2011)
Lei, X.-J., Sun, J.-J., Ma, Q.-Z.: Multiple Sequence Alignment Based on Chaotic PSO. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. CCIS, vol. 51, pp. 351–360. Springer, Heidelberg (2009)
Gao, W.F., Liu, S.Y., Jiang, F.: An improved artificial bee colony algorithm for directing orbits of chaotic systems. Applied Mathematics and Computation 218, 3868–3879 (2011)
Gandomi, A.H., Yang, X.S.: Chaotic bat algorithm. Journal of Computational Science 5(2), 224–232 (2014)
Coelho, L., Mariani, V.C.: Use of chaotic sequences in biologically inspired algorithm for engineering design optimization. Expert Systems with Applications 34, 1905–1913 (2008)
May, R.: Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976)
Zerzucha, P., Walczak, B.: Concept of (dis)similarity in data analysis. TrAC Trends in Analytical Chemistry (38), 116–128 (2012)
Lei, X.J., Huang, X., Zhang, A.D.: Improved Artificial Bee Colony Algorithm and Its Application in Data Clustering. In: The IEEE Fifth International Conference on Bio-Inspired Computing, Theories and Applications (BIC-TA 2010), Changsha, China, pp. 514–521 (2010)
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Lei, X., Du, M., Xu, J., Tan, Y. (2014). Chaotic Fruit Fly Optimization Algorithm. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_9
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DOI: https://doi.org/10.1007/978-3-319-11857-4_9
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