A NEW KIND OF PSO: PREDATOR PARTICLE SWARM OPTIMIZATION

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 5 , ISSUE 2 (June 2012) > List of articles

A NEW KIND OF PSO: PREDATOR PARTICLE SWARM OPTIMIZATION

Mehdi Neshat * / Mehdi Sargolzaei * / Azra Masoumi * / Adel Najaran *

Keywords : Predator, particle swarm optimization, local optimum, premature convergence.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 5, Issue 2, Pages 521-539, DOI: https://doi.org/10.21307/ijssis-2017-493

License : (CC BY-NC-ND 4.0)

Received Date : 17-March-2012 / Accepted: 11-May-2012 / Published Online: 01-June-2012

ARTICLE

ABSTRACT

Today, swarm intelligence is widely used in optimization problems. PSO is one the best swarm intelligence methods. In the method, each particle moves toward the direction in which the best individual and group experience has happened. The most important disadvantage of this method is that it falls in local optima. To fix the problem, a metaheuristic method is proposed in this paper. There has always been a competition between prey and predator in the nature. Little birds often fly in a colony form to run away from birds of prey. Being inspired by the phenomenon, a new particle is added to PSO algorithm known as predator, also a new behavior called “Take flight from predator" is defined. This particle is responsible for attacking the colony of particles so as to prevent the premature convergence. With the predator attack to the colony, particles run away and again the chance rises for a Global optimum to be gained. The attack just caused particles dispersion and no particle dies. It can be repeated for m times and the optimal point is saved each time. To test the method, 12 benchmark functions were employed and the results were compared to OPSO, VPSO, LPSO, and GPSO methods. Regarding the results, the proposed method had a better performance.

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REFERENCES

[1] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw., Perth, Australia, 1995, vol. 4, pp. 1942–1948.
[2] R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micromachine Human Sci., Nagoya,Japan, 1995, pp. 39–43.
[3] J. Kennedy, R. C. Eberhart, and Y. H. Shi, Swarm Intelligence. San Mateo, CA: Morgan Kaufmann, 2001.
[4] R. C. Eberhart and Y. H. Shi, “Particle swarm optimization: Developments, applications and resources,” in Proc. IEEE Congr. Evol. Comput.,Seoul, Korea, 2001, pp. 81–86.
[5] X. D. Li and A. P. Engelbrecht, “Particle swarm optimization: An introduction and its recent developments,” in Proc. Genetic Evol. Comput.Conf., 2007, pp. 3391–3414.
[6] S.-Y. Ho, H.-S. Lin, W.-H. Liauh, and S.-J. Ho, “OPSO: Orthogonal particle swarm optimization and its application to task assignment problems,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 38, no. 2, pp. 288–298, Mar. 2008.
[7] B. Liu, L. Wang, and Y. H. Jin, “An effective PSO-based memetic algorithm for flow shop scheduling,” IEEE Trans. Syst., Man, Cybern. B,Cybern., vol. 37, no. 1, pp. 18–27, Feb. 2007.
[8] R. C. Eberhart and Y. Shi, “Guest editorial,” IEEE Trans. Evol. Comput.—Special Issue Particle Swarm Optimization, vol. 8, no. 3,pp. 201–203, Jun. 2004.
[9] G. Ciuprina, D. Ioan, and I. Munteanu, “Use of intelligent-particle swarm optimization in electromagnetics,” IEEE Trans. Magn., vol. 38, no. 2,pp. 1037–1040, Mar. 2002.
[10] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281–295,Jun. 2006.
[11] Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” in Proc. IEEE World Congr. Comput. Intell., 1998, pp. 69–73.
[12] Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., 2001, vol. 1, pp. 101–106.
[13] A. Ratnaweera, S. Halgamuge, and H. Watson, “Particle swarm optimization with self-adaptive acceleration coefficients,” in Proc. 1st Int. Conf.Fuzzy Syst. Knowl. Discovery, 2003, pp. 264–268.
[14] P. K. Tripathi, S. Bandyopadhyay, and S. K. Pal, “Adaptive multi-objective particle swarm optimization algorithm,” in Proc. IEEE Congr. Evol.Comput., Singapore, 2007, pp. 2281–2288.
[15] P. J. Angeline, “Using selection to improve particle swarm optimization,”in Proc. IEEE Congr. Evol. Comput., Anchorage, AK, 1998, pp. 84–89.
[16] Y. P. Chen,W. C. Peng, andM. C. Jian, “Particle swarm optimization with recombination and dynamic linkage discovery,” IEEE Trans. Syst., Man,Cybern. B, Cybern., vol. 37, no. 6, pp. 1460–1470, Dec. 2007.
[17] P. S. Andrews, “An investigation into mutation operators for particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., Vancouver,BC, Canada, 2006, pp. 1044–1051.
[18] J. J. Liang and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer with local search,” in Proc. IEEE Congr. Evol. Comput., 2005,pp. 522–528.
[19] A. Carlisle and G. Dozier, “Adapting particle swarm optimization to dynamic environments,” in Proc. Int. Conf. Artif. Intell., Las Vegas, NV,2000, pp. 429–434.
[20] X. Hu and R. C. Eberhart, “Adaptive particle swarm optimization: Detection and response to dynamic systems,” in Proc. IEEE Congr. Evol.Comput., Honolulu, HI, 2002, pp. 1666–1670.
[21] X. Xie, W. Zhang, and Z. Yang, “Adaptive particle swarm optimization on individual level,” in Proc. Int. Conf. Signal Process., 2002,pp. 1215–1218.
[22] M. Clerc, “The swarm and the queen: Toward a deterministic and adaptive particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., 1999,pp. 1951–1957.
[23] Zhi-Hui Zhan, Jun Zhang, Yun Li,Henry Shu-Hung Chung, “Adaptive Particle Swarm Optimization”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, 39 Issue:6 , 1362 – 1381,2009.
[24] E. O. Wilson, Sociobiology: the new synthesis, Belknap Press, Cambridge, MA, 1975.
[25] Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” in Proc. IEEE World Congr. Comput. Intell., 1998, pp. 69–73.
[26] M. Clerc and J. Kennedy, “The particle swarm-explosion, stability and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput., vol. 6, no. 1, pp. 58–73, Feb. 2002.
[27] I. C. Trelea, “The particle swarm optimization algorithm: Convergence analysis and parameter selection,” Inf. Process. Lett., vol. 85, no. 6, pp. 317–325, Mar. 2003.
[28] K. Yasuda, A. Ide, and N. Iwasaki, “Stability analysis of particle swarm optimization,” in Proc. 5th Metaheuristics Int. Conf., 2003, pp. 341–346.
[29] V. Kadirkamanathan, K. Selvarajah, and P. J. Fleming, “Stability analysis of the particle dynamics in particle swarm optimizer,” IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 245–255, Jun. 2006.
[30] F. van den Bergh and A. P. Engelbrecht, “A study of particle optimization particle trajectories,” Inf. Sci., vol. 176, no. 8, pp. 937–971, Apr. 2006.
[31] Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., 1999, pp. 1945–1950.
[32] Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., 2001, vol. 1, pp. 101–106.
[33] R. C. Eberhart and Y. Shi, “Tracking and optimizing dynamic systems with particle swarms,” in Proc. IEEE Congr. Evol. Comput., Seoul, Korea, 2001, pp. 94–97.
[34] M. Clerc, “The swarm and the queen: Toward a deterministic and adaptive particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., 1999, pp. 1951–1957.
[35] M. Clerc and J. Kennedy, “The particle swarm-explosion, stability and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput., vol. 6, no. 1, pp. 58–73, Feb. 2002.
[36] R. C. Eberhart and Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., 2000, pp. 84–88.
[37] J. Kennedy, “The particle swarm social adaptation of knowledge,” in Proc. IEEE Int. Conf. Evol. Comput., Indianapolis, IN, Apr. 1997, pp. 303–308.
[38] P. N. Suganthan, “Particle swarm optimizer with neighborhood operator,” in Proc. IEEE Congr. Evol. Comput., Washington DC, 1999, pp. 1958–1962.
[39] A. Ratnaweera, S. Halgamuge, and H. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 240–255, Jun. 2004.
[40] P. J. Angeline, “Using selection to improve particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., Anchorage, AK, 1998, pp. 84–89.
[41] C. F. Juang, “A hybrid of genetic algorithm and particle swarm optimization for recurrent network design,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 2, pp. 997–1006, Apr. 2004.
[42] Y. P. Chen,W. C. Peng, andM. C. Jian, “Particle swarm optimization with recombination and dynamic linkage discovery,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 6, pp. 1460–1470, Dec. 2007.
[43] P. S. Andrews, “An investigation into mutation operators for particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., Vancouver, BC, Canada, 2006, pp. 1044–1051.
[44] J. J. Liang and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer with local search,” in Proc. IEEE Congr. Evol. Comput., 2005, pp. 522–528.
[45] W. J. Zhang and X. F. Xie, “DEPSO: Hybrid particle swarm with differential evolution operator,” in Proc. IEEE Conf. Syst., Man, Cybern., Oct. 2003, pp. 3816–3821.
[46] F. van den Bergh and A. P. Engelbrecht, “A cooperative approach to particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 225–239, Jun. 2004.
[47] A. Ratnaweera, S. Halgamuge, and H. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 240–255, Jun. 2004.
[48] K. E. Parsopoulos and M. N. Vrahatis, “On the computation of all global minimizers through particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 211–224, Jun. 2004.
[49] R. Brits, A. P. Engelbrecht, and F. van den Bergh, “A niching particle swarm optimizer,” in Proc. 4th Asia-Pacific Conf. Simul. Evol. Learn., 2002, pp. 692–696.
[50] R. Brits, A. P. Engelbrecht, and F. van den Bergh, “Locating multiple optima using particle swarm optimization,” Appl. Math. Comput., vol. 189, no. 2, pp. 1859–1883, Jun. 2007.
[51] D. Parrott and X. D. Li, “Locating and tracking multiple dynamic optima by a particle swarm model using speciation,” IEEE Trans. Evol. Comput., vol. 10, no. 4, pp. 440–458, Aug. 2006.
[52] J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proc. IEEE Congr. Evol. Comput., Honolulu, HI, 2002, pp. 1671–1676.
[53] J. Kennedy and R. Mendes, “Neighborhood topologies in fully informed and best-of-neighborhood particle swarms,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 36, no. 4, pp. 515–519, Jul. 2006.
[54] X. Hu and R. C. Eberhart, “Multiobjective optimization using dynamic neighborhood particle swarm optimization,” in Proc. IEEE Congr. Evol. Comput., Honolulu, HI, 2002, pp. 1677–1681.
[55] J. J. Liang and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer,” in Proc. Swarm Intell. Symp., Jun. 2005, pp. 124–129.
[56] R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: Simpler, maybe better,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 204–210, Jun. 2004.
[57] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281–295, Jun. 2006.
[58] Zhi-Hui Zhan, , Jun Zhang, , Yun Li, Henry Shu-Hung Chung, “Adaptive Particle Swarm Optimization”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, 2009.
[59] J. Kennedy and R. Mendes, “Neighborhood topologies in fully informed and best-of-neighborhood particle swarms,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 36, no. 4, pp. 515–519, Jul. 2006.
[60] J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proc. IEEE Congr. Evol. Comput., Honolulu, HI, 2002, pp. 1671–1676.
[61] Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” in Proc. IEEE World Congr. Comput. Intell., 1998, pp. 69–73.
[62] S.-Y. Ho, H.-S. Lin, W.-H. Liauh, and S.-J. Ho, “OPSO: Orthogonal particle swarm optimization and its application to task assignment problems,”IEEE Trans. Syst., Man, Cybern. A, vol. 38, no. 2, pp. 288–298,Mar. 2008.

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