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Recent Advances in Particle Swarm Optimization for Large Scale Problems

Danping Yan, Yongzhong Lu

Abstract


Accompanied by the advent of current big data ages, the scales of real world optimization problems with many decisive design variables are becoming much larger. Up to date, how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation. So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics. As a branch of the swarm intelligence based algorithms, particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over the last decade years. This review paper mainly presents its recent achievements and trends, and also highlights the existing unsolved challenging problems and key issues with a huge impact in order to encourage further more research in both large scale PSO theories and their applications in the forthcoming years.

Keywords


swarm intelligence; particle swarm optimization; large scale optimization problem; cooperative coevolution; ensemble evolution; static grouping method; dynamic grouping method.

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References


Ali, Y. M. B., Soft adaptive particle swarm algorithm for large scale optimization, in: IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), IEEE, 2010, pp. 1658-1662.

Aziz, M., Tayarani-N., M.-H., An adaptive memetic particle swarm optimization algorithm for finding large-scale Latin hypercube designs, Engineering Applications of Artificial Intelligence 36 (2014) 222-237.

Banka, H., Dara, S., A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation, Pattern RecognitionLetters 52 (2015) 94-100.

Budhraja, K. K., Singh, A., Dubey, G., Khosla, A., Exploration enhanced particle swarm optimization using guided reinitialization, in: Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), Springer, 2013, pp. 403-416.

Cai, Q., Gong, M., Ma, L., Ruan, S., Yuan, F., Jiao, L., Greedy discrete particle swarm optimization for large-scale social network clustering, Information Sciences 316 (2015) 503-

Chen, K.-T., Dai, Y., Fan, K., Baba, T., A particle swarm optimization with adaptive multi-swarm strategy for capacitated vehicle routing problem, in: IEEE 1st International Conference on Industrial Networks and Intelligent Systems (INISCom), IEEE, 2015, pp. 79-83.

Cheng, R., Xu, L., Liu, Y., Gao, J., Distribution network reconfiguration based on adaptive bi-group particle swarm algorithm, in: 8th International Symposium on Computational Intelligence and Design (ISCID), 2015, vol. 1, pp. 374-378.

Cheng, R., Jin, Y., A competitive swarm optimizer for large scale optimization, IEEE Transactions on Cybernetics 45 (2) (2015) 191-204.

Cheng, R., Jin, Y., A social learning particle swarm optimization algorithm for scalable optimization, Information Sciences 291 (2015) 43-60.

Chu, Y., Mi, H., Liao, H., Ji, Z., Wu, Q. H., A fast bacterial swarming algorithm for high-dimensional function optimization, in: IEEE Congress on Evolutionary Computation, IEEE, 2008, pp. 3135-3140.

Chu, W., Gao, X., Sorooshian, S., Handling boundary constraints for particle swarm optimization in high- dimensional search space, Information Sciences 181 (20) (2011) 4569-4581.

A. P., Scalability of a heterogeneous particle swarm optimizer, in: IEEE Symposium on Swarm Intelligence, IEEE, 2011, pp. 1-8.

Fan, J., Wang, J., Han, M., Cooperative coevolution for large- scale optimization based on kernel fuzzy clustering and variable trust region methods, IEEE Transactions on Fuzzy Systems 22 (4) (2014) 829-839.

Garc?a-Nieto, J., Alba, E., Restart particle swarm optimization with velocity modulation: a scalability test, Soft Computing 15 (11) (2011) 2221-2232.

Gong, M., Wu, Y., Cai, Q., Ma, W., Qin, A. K., Wang, Z., Jiao, L.,

Discrete particle swarm optimization for high-order graph matching, Information Sciences 328 (2016) 158-171.

Hou, P., Hu, W., Soltani, M., Chen, Z., Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm, IEEE Transactions on Subtainable Energy 6 (4) (2015) 1272-1282.

Hsieh, S.-T., Sun, T.-Y., Liu, C.-C., Tsai, S.-J., Solving large scale global optimization using improved particle swarm optimizer, in: IEEE Congress on Evolutionary Computation, IEEE, 2008, pp. 1777-1784.

Ismail, A., Engelbrecht, A. P., Measuring diversity in the cooperative particle swarm optimizer, in: Swarm Intelligence, Lecture Notes in Computer Science, Springer, 2012, vol. 7461, pp. 97-108.

Jiang, B., Wang, N., Cooperative bare-bone particle swarm optimization for data clustering, Soft Computing 18 (6) (2014) 1079-1091.

Jiao, B., Chen, Q., Yan, S., A cooperative coevolution pso for flow shop scheduling problem with uncertainty, Journal of Computers 6 (9) (2011) 1955-1961.

Lee, S.-M., Kim, H., Myung, H., Yao, X., Cooperative coevolutionary algorithm-based model predictive control guaranteeing stability of multirobot formation, IEEE Transactions on Control Systems Technology 23 (1) (2015)

-51.

Li, X., Yao, X., Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms, in: IEEE Congress on Evolutionary Computation, IEEE, 2009, pp. 1546-1553.

Li, X., Yao, X., Cooperatively coevolving particle swarms for large scale optimization, IEEE Transactions on Evolutionary Computation 16 (2) (2012) 210-224.

Li, Z., Wang, W., Yan, Y., Li, Z., PS-ABC: A hybrid algorithm based on particle swarm and artificial bee colony for high- dimensional optimization problems, Expert Systems With Applications 42 (2015) 8881-8895.

Lin, L., Gen, M., Liang, Y., A hybrid EA for high-dimensional subspace clustering problem, in: IEEE Congress on Evolutionary Computation, IEEE, 2014, pp. 2855-2860.

Montes de Oca, M. A., Stutzle, T., Van den Enden, K., Dorigo, M., Incremental social learning in particle swarms, IEEE Transactions on System. Man and Cybernetics, Part B: Cybernetics 41 (2) (2011) 368-384.

Montes de Oca, M. A., Aydin, D., Stutzle, T., An incremental particle swarm for large-scale continuous optimization problems: an example of tuning-in-the-loop (re)design of optimization algorithms, Soft Computing 15 (11) (2011)2233-2255.

Ouyang, H.-b., Gao, L.-q., Kong, X.-y., Li, S., Zou, D.-x., Hybrid harmony search particle swarm optimization with global dimension selection, Information Sciences 346-347 (2016) 318-337.

Rather, Z. H., Chen, Z., Thgersen, P., Lund, P., Dynamic reactive power compensation of large-scale wind integrated power system, IEEE Transactions on Power Systems 30 (5) (2015) 2516-2526.

Sahu, P. K., Shah, T., Manna, K., Chattopadhyay, S., Application mapping onto mesh-based network-on- chip using discrete particle swarm optimization, IEEE Transactions on Very Large Scale Integration (VLSI) Systems 22 (2) (2014) 300-312.

Sun, C., Tao, H., Guo, X., Xie, J., Adaptive interferences suppression algorithm after subarray configuration for large-scale antenna array, IET Electronics Letters 52 (1) (2016) 7-8.

Sun, L., Yoshida, S., Cheng, X., Liang, Y., A cooperative particle swarm optimizer with statistical variable interdependence learning, Information Sciences 186 (1)(2012) 20-39.

Tang, D., Cai, Y., Zhao, J., Xue, Y., A quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems, Information Sciences 277 (2014) 680-693.

Van den Bergh, F., Engelbrecht, A. P., A cooperative approach to particle swarm optimization, IEEE Transactions on Evolutionary Computation 8 (3) (2004) 225-239.

Van Zyl, E., Engelbrecht, A. P., A subspace-based method for PSO initialization, in: IEEE Symposium Series on Computational Intelligence, IEEE, 2015, pp. 226-233.

Van Zyl, E., Engelbrecht, A. P., Group-based stochastic scaling for PSO velocities, in: IEEE Congress on Evolutionary Computation, 2016, pp. 66-73.

Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., Ventresca, M., Enhancing particle swarm optimization using generalized opposition based learning, Information Sciences 181 (20) (2011) 4699-4714.

Wang, H., Sun, H., Li, C., Rahnamayan, S., Pan, J., Diversity enhanced particle swarm optimization with neighborhood search, Information Sciences 223 (2013) 119-135.

Zhang, C., Yi, Z., Scale-free fully informed particle swarm optimization algorithm, Information Sciences 181 (20)(2011) 4550-4568.

Zhang, Y., Jing, Z., Zhang, Y., MR-IDPSO: a novel algorithm for large-scale dynamic service composition, Tsinghua Science and Technology 20 (6) (2015) 602-612.

Zhang, W.-X., Chen, W.-N., Zhang, J., A dynamic competitive swarm optimizer based-on entropy for large scale optimization, in: IEEE Eighth International Conference on Advanced Computational Intelligence (ICACI), IEEE, 2016, pp. 365-371.

Zhao, S.-Z., Liang, J. J., Suganthan, P. N., Tasgetiren, M. F., Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization, in: IEEE Congress on Evolutionary Computation, IEEE, 2008, pp. 3845-3852.

Zhao, S.-Z., Suganthan, P. N., Das, S., Dynamic multi-swarm particle swarm optimizer with sub-regional harmony search, in: IEEE Congress on Evolutionary Computation, IEEE, 2010, pp. 1-8.




DOI: http://dx.doi.org/10.32629/jai.v1i1.15

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