A study on the effect of vmax in particle swarm optimisation with high dimension

Velocity threshold v max is an important parameter of particle swarm optimisation. Different from other parameters, it affects the algorithm performance by restricting the moving size and direction of each particle. However, the current results are all with small dimensions no larger than 30. Because of the scientific development, many optimisation tasks are complex, high dimensional multi-modal functions. Therefore, in this paper, the authors investigate the selection principle of v max with high dimension on numerical optimisation problems. To make a deep insight, the test suit consists of three different type benchmarks: unimodel, multi-modal functions with a few local optima and multi-modal functions with many local optima. Simulation results show the 10% of the upper bound of the domain may generally obtain the satisfied solution within the allowed iterations.

[1]  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.

[2]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

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

[4]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[5]  F. van den Bergh,et al.  Training product unit networks using cooperative particle swarm optimisers , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[6]  Tiago Ferra de Sousa,et al.  Swarm optimisation as a new tool for data mining , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[7]  Tiago Ferra de Sousa,et al.  A Particle Swarm Data Miner , 2003, EPIA.

[8]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[9]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[10]  R. K. Suresh,et al.  Discrete Particle Swarm Optimization (DPSO) Algorithm for Permutation Flowshop Scheduling to Minimize Makespan , 2005, ICNC.

[11]  Jiao Li-cheng,et al.  Intelligent particle swarm optimization in multiobjective optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[12]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[13]  Jigui Sun,et al.  A Hybrid Particle Swarm Optimization for Binary CSPs , 2006, ICIC.

[14]  Yun Shang,et al.  A Note on the Extended Rosenbrock Function , 2006 .