Autonomous particles groups for particle swarm optimization. (English) Zbl 1390.90590
Summary: In this paper, a modified particle swarm optimization (PSO) algorithm called autonomous groups particles swarm optimization (AGPSO) is proposed to further alleviate the two problems of trapping in local minima and slow convergence rate in solving high-dimensional problems. The main idea of AGPSO algorithm is inspired by individuals’ diversity in bird flocking or insect swarming. In natural colonies, individuals are not basically quite similar in terms of intelligence and ability, but they all do their duties as members of a colony. Each individual’s ability can be useful in a particular situation. In this paper, a mathematical model of diverse particles groups called autonomous groups is proposed. In other words different functions with diverse slopes, curvatures, and interception points are employed to tune the social and cognitive parameters of the PSO algorithm to give particles different behaviors as in natural colonies. The results show that PSO with autonomous groups of particles outperforms the conventional and some recent modifications of PSO in terms of escaping local minima and convergence speed. The results also indicate that dividing particles in groups and allowing them to have different individual and social thinking can improve the performance of PSO significantly.
MSC:
| 90C59 | Approximation methods and heuristics in mathematical programming |
Keywords:
PSO; social behavior; social coefficient; cognitive coefficient; function optimization; autonomous particles groupsReferences:
| [1] | Eberhart, R.C.; Kennedy, J.: A new optimizer using particles swarm theory. In: Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39-43 (1995) · Zbl 1386.65165 |
| [2] | Eberhart R.C.; Kennedy, J.: Particle swarm optimization. In: IEEE International Conference on Neural Network, Perth, Australia, pp. 1942-1948 (1995) |
| [3] | Chandra, S.; Bhat, R.; Singh, H.: A PSO based method for detection of brain tumors from MRI. In: Nature & Biologically Inspired Computing, Coimbatore, pp. 666-671 (2009) |
| [4] | Mathiyalagan, M.; Dhepthie, U.; Sivanandam, S.: Grid Scheduling Using Enhanced PSO Algorithm. vol. 2, pp. 140-145 (2010) |
| [5] | Masehian, E.; Sedighizadeh, D.: A multi-objective PSO-based algorithm for robot path planning. In: IEEE International Conference on Industrial Technology Vi a del Mar, pp. 465-470 (2010) |
| [6] | Fayk, M.; El Nemr, H.; Moussa, M., Particle swarm optimisation based video abstraction, J. Adv. Res., 1, 163-167, (2010) · doi:10.1016/j.jare.2010.03.009 |
| [7] | Mirjalili, S.; Rawlins, T.; Hettenhausen, J.; Lewis, A., A comparison of multi-objective optimisation metaheuristics on the 2D airfoil design problem, ANZIAM J., 54, c345-c360, (2013) · Zbl 1386.65165 |
| [8] | Mirjalili, S.; Sadiq, A.S.: Magnetic Optimization algorithm for training multi layer perceptron. In: 2011 IEEE 3rd international conference on communication software and networks (ICCSN), pp. 42-46 (2011) · Zbl 0984.65004 |
| [9] | Mirjalili, S.; Mohd Hashim, S.Z.; Moradian Sardroudi, H.: Training feed forward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput. 218, 11125-11137 (2012) · Zbl 1282.90248 |
| [10] | Mirjalili, S.; Mirjalili, S.M.; Lewis, A., Let a biogeography-based optimizer train your multi-layer perceptron, Inform Sci., 269, 188-209, (2014) · doi:10.1016/j.ins.2014.01.038 |
| [11] | Mirjalili, S.; Lewis, A.: S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1-14 (2013) |
| [12] | Mirjalili, S.; Lewis, A.: Adaptive gbest guided Gravitational Search Algorithm. Neural Comput. Appl. (2014). doi:10.1007/s00521-014-1640-y |
| [13] | Saremi, S.; Mirjalili, S.; Lewis, A.: Biogeography-based optimisation with chaos. Neural Comput. Appl. 1-21 (2014). doi:10.1007/s00521-014-1597-x |
| [14] | Wang, G.; Guo, L.; Wang, H.; Duan, H.; Liu, L.; Li, J.: Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput. Appl. 1-19 (2012) |
| [15] | Wang, G.-G.; Gandomi, A.H.; Alavi, A.H.: A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes 42, 9-9 (2013) · Zbl 1511.90432 |
| [16] | Wang, G.-G.; Gandomi, A.H.; Alavi, A.H.: An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl. Math. Model. (2013) · Zbl 1427.90303 |
| [17] | Wang, G.-G.; Gandomi, A.H.; Alavi, A.H., Stud krill herd algorithm, Neurocomputing, 128, 363-370, (2014) · doi:10.1016/j.neucom.2013.08.031 |
| [18] | Wang, G.-G.; Gandomi, A.H.; Alavi, A.H.; Hao, G.-S.: Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput. Appl. 1-12 (2013). doi:10.1007/s00521-013-1485-9 |
| [19] | Wang, G.-G.; Guo, L.; Gandomi, A.H.; Hao, G.-S.; Wang, H., Chaotic krill herd algorithm, Inform. Sci., 274, 17-34, (2014) · doi:10.1016/j.ins.2014.02.123 |
| [20] | Guo, L.; Wang, G.-G.; Gandomi, A.H.; Alavi, A.H.; Duan, H., A new improved krill herd algorithm for global numerical optimization, Neurocomputing, 138, 392-402, (2013) · doi:10.1016/j.neucom.2014.01.023 |
| [21] | Saremi, S.; Mirjalili, S., Integrating chaos to biogeography-based optimization algorithm, Int. J. Comput. Commun. Eng., 2, 655-658, (2013) · doi:10.7763/IJCCE.2013.V2.268 |
| [22] | Saremi, S.; Mirjalili, S. M.; Mirjalili, S., Chaotic krill herd optimization algorithm, Procedia Technol., 12, 180-185, (2014) · doi:10.1016/j.protcy.2013.12.473 |
| [23] | Premalatha, K.; Natarajan, A.M., Hybrid PSO and GA for global maximization, Int. J. Open Probl. Comput. Sci Math., 2, 597-608, (2009) · Zbl 1206.90225 |
| [24] | Mirjalili, S.; Mohd Hashim, S.Z.: A new hybrid PSOGSA algorithm for function optimization. In: International Conference on Computer and Information Application (ICCIA 2010), pp. 374-377 (2010) |
| [25] | Shuang, B.; Chen, J.; Li, Z.: Study on hybrid PS-ACO algorithm. Appl. Intell. 34, pp. 64-73 (2011) |
| [26] | Toscano-Pulido, G.; Reyes-Medina, A.; Ramirez-Torres, J., A statistical study of the effects of neighborhood topologies in particle swarm optimization, Comput. Intell., 343, 179-192, (2011) · doi:10.1007/978-3-642-20206-3_12 |
| [27] | Matsushita, H.; Nishio, Y., Network-structured particle swarm optimizer with various topology and its behaviors, Adv. Self-Organ. Maps., 5629, 163-171, (2009) · doi:10.1007/978-3-642-02397-2_19 |
| [28] | Mo, S.; Zeng, J.; Tan, Y., Particle swarm optimisation based on self-organisation topology driven by different fitness rank, Int. J. Comput. Sci. Eng., 6, 24-33, (2011) · doi:10.1504/IJCSE.2011.041209 |
| [29] | Cai, X.: A new modified PSO based on black stork foraging process. In: 8th IEEE international conference on cognitive informatics, Kowloon, Hong Kong, pp. 509-513 (2009) |
| [30] | Caia, X.; Cui, Z.; Zeng, J.; Tana, Y., Dispersed particle swarm optimization, Inform. Process. Lett., 105, 231-235, (2008) · Zbl 1189.90205 · doi:10.1016/j.ipl.2007.09.001 |
| [31] | Cai, X.; Cui, Y.; Tan, Y., Predicted modified PSO with time-varying accelerator coefficients, Int. J. Bio-Inspired Comput., 1, 50-60, (2009) · doi:10.1504/IJBIC.2009.022773 |
| [32] | Ziyu, T.; Dingxue, Z.: A modified particle swarm optimization with an adaptive acceleration coefficients. In: Asia-Pacific Conference on Information Processing, Shenzhen, pp. 330-332 (2009) |
| [33] | Bao, G.Q.; Mao, K.F.: Particle swarm optimization algorithm with asymmetric time varying acceleration coefficients. In: IEEE International Conference on Robotics and Biomimetics, Guilin, pp. 2134-2139 (2009) |
| [34] | Cui, Z.; Zeng, J.; Yin, Y.: An improved PSO with time-varying accelerator coefficients. In: Eighth international conference on intelligent systems design and applications, Kaohsiung, pp 638-643 (2008) |
| [35] | Fukuyama, Y.; Yoshida, H.: A particle swarm optimization for reactive power and voltage control in electric power system. In: IEEE congress on evolutionary computation, Seoul, Korea, pp. 87-93 (2001) |
| [36] | Molga, M.; Smutnicki, C.: In: Test functions for optimization needs (2005) |
| [37] | Yang, X.-S.: In: An introduction with metaheuristic applications Xin-She, Y. (ed.) Test Problems in Optimization. Wiley, New York (2010) |
| [38] | Yao, X.; Liu, Y.; Lin, G., Evolutionary programming made faster, IEEE Tran. Evol. Comput., 3, 82-102, (1999) · doi:10.1109/4235.771163 |
| [39] | Digalakis, J.G.; Margaritis, K.G., On benchmarking functions for genetic algorithms, Intern. J. Comput. Math., 77, 481-506, (2001) · Zbl 0984.65004 · doi:10.1080/00207160108805080 |
| [40] | Mirjalili, S.; Mirjalili, S.M.; Lewis, A., Grey wolf optimizer, Adv. Eng. Soft., 69, 46-61, (2014) · doi:10.1016/j.advengsoft.2013.12.007 |
| [41] | Mirjalili, S.; Mirjalili, S.M.; Yang, X.-S.: Binary bat algorithm. Neural Comput. Appl. 1-19 (2013). doi:10.1007/s00521-013-1525-5 |
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