### Recent Advances in Particle Swarm Optimization for Large Scale Problems

#### 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

#### Full Text:

PDF#### 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.

, 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 highdimensional

search space, Information Sciences 181 (20)

(2011) 4569-4581.

Engelbrecht, 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´ıa-Nieto, J., Alba, E., Restart particle swarm optimization

with velocity modulation: a scalability test, Soft Computing

(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,

, 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)

-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., Thøgersen, 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.

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)

-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., Thøgersen, 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.

DOI: http://dx.doi.org/10.63019/jai.v1i1.19

### Refbacks

- There are currently no refbacks.

Copyright (c) 2018 Yongzhong Lu, Danping Yan

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.