Multi-level Competitive Swarm Optimizer for Large Scale Optimization

In this paper, a new multi-level competitive swarm optimizer (MLCSO) is proposed for large scale optimization. As a variant of particle swarm optimization (PSO), MLCSO first divides the particles of original swarm into two groups randomly and then compares the particles according to their fitness values. The loser with worse fitness value will be put into the first level. The winner with better fitness becomes a new little swarm. New little swarm continues to be divided and compared until the new swarm has only one particle. This process forms a multi-level mechanism. The loser will be updated by the winner. It not only shows a great balance between exploration and exploitation but also enhances the diversity. 20 different kinds of test functions are selected for the experiments. Despite MLCSO algorithm is simple, the experimental results on high-dimension by comparing it with five state-of-the-art algorithms demonstrated its effectiveness.

[1]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[2]  Feng Zhao,et al.  A Cooperative Co-Evolutionary Approach to Large-Scale Multisource Water Distribution Network Optimization , 2019, IEEE Transactions on Evolutionary Computation.

[3]  Yuan Sun,et al.  Extended Differential Grouping for Large Scale Global Optimization with Direct and Indirect Variable Interactions , 2015, GECCO.

[4]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[6]  Russell C. Eberhart,et al.  Humans—Actual, Imagined, and Implied , 2001 .

[7]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[8]  Keiichiro Yasuda,et al.  Adaptive particle swarm optimization , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[9]  Xin Yao,et al.  Multilevel cooperative coevolution for large scale optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[10]  Xin Yao,et al.  Large scale evolutionary optimization using cooperative coevolution , 2008, Inf. Sci..

[11]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[12]  Yaochu Jin,et al.  A Competitive Swarm Optimizer for Large Scale Optimization , 2015, IEEE Transactions on Cybernetics.

[13]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[14]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[15]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[16]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[17]  Jun Zhang,et al.  Adaptive Multimodal Continuous Ant Colony Optimization , 2017, IEEE Transactions on Evolutionary Computation.

[18]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[19]  Jun Zhang,et al.  Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization , 2017, IEEE Transactions on Cybernetics.

[20]  Xiaodong Li,et al.  Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization , 2014, IEEE Transactions on Evolutionary Computation.

[21]  Mitchell A. Potter,et al.  The design and analysis of a computational model of cooperative coevolution , 1997 .

[22]  Xiaodong Li,et al.  Cooperatively Coevolving Particle Swarms for Large Scale Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[23]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[24]  Jun Zhang,et al.  A Level-Based Learning Swarm Optimizer for Large-Scale Optimization , 2018, IEEE Transactions on Evolutionary Computation.

[25]  Jun Zhang,et al.  A Dynamic Logistic Dispatching System With Set-Based Particle Swarm Optimization , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[26]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[27]  Xiaodong Li,et al.  A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization , 2016, ACM Trans. Math. Softw..

[28]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .