Boundary Constraint Handling Techniques for Particle Swarm Optimization in High Dimensional Problem Spaces

This paper investigates the use of boundary constraint handling mechanisms to prevent unwanted particle roaming behaviour in high dimensional spaces. The paper tests a range of strategies on a benchmark for large scale optimization. The empirical analysis shows that the hyperbolic strategy, which scales down a particle’s velocity as it approaches the boundary, performs statistically significantly better than the other methods considered in terms of the best objective function value achieved. The hyperbolic strategy directly addresses the velocity explosion, thereby preventing unwanted roaming.

[1]  Kalyanmoy Deb,et al.  Optimization for Engineering Design: Algorithms and Examples , 2004 .

[2]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[3]  Wei Chu,et al.  Handling boundary constraints for particle swarm optimization in high-dimensional search space , 2011, Inf. Sci..

[4]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[5]  Jürgen Branke,et al.  Experimental Analysis of Bound Handling Techniques in Particle Swarm Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[6]  Rolf Wanka,et al.  Theoretical Analysis of Initial Particle Swarm Behavior , 2008, PPSN.

[7]  Xiao-Feng Xie,et al.  Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  Yuhui Shi,et al.  Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective , 2011, Int. J. Swarm Intell. Res..

[9]  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).

[10]  Zbigniew Michalewicz,et al.  Impacts of Coefficients on Movement Patterns in the Particle Swarm Optimization Algorithm , 2017, IEEE Transactions on Evolutionary Computation.

[11]  Rolf Wanka,et al.  Particle Swarm Optimization in High-Dimensional Bounded Search Spaces , 2007, 2007 IEEE Swarm Intelligence Symposium.

[12]  A. Ravindran,et al.  Engineering Optimization: Methods and Applications , 2006 .

[13]  Jonathan E. Fieldsend,et al.  A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts , 2005, EMO.

[14]  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.

[15]  Xiaodong Li,et al.  Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale , 2009 .

[16]  Mostaghim Sanaz,et al.  Linear Multi-Objective Particle Swarm Optimization , 2006 .

[17]  Kalyanmoy Deb,et al.  Boundary Handling Approaches in Particle Swarm Optimization , 2012, BIC-TA.

[18]  Y. Rahmat-Samii,et al.  Particle swarm optimization in electromagnetics , 2004, IEEE Transactions on Antennas and Propagation.

[19]  T. Huang,et al.  A hybrid boundary condition for robust particle swarm optimization , 2005, IEEE Antennas and Wireless Propagation Letters.

[20]  Andries Petrus Engelbrecht,et al.  Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption , 2018, Swarm Intelligence.