Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.

[1]  Mohammad Mehdi Ebadzadeh,et al.  A novel particle swarm optimization algorithm with adaptive inertia weight , 2011, Appl. Soft Comput..

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

[3]  S.F. Mekhamer,et al.  A Modified Particle Swarm Optimizer for the Coordination of Directional Overcurrent Relays , 2007, IEEE Transactions on Power Delivery.

[4]  Jing Bai,et al.  Different inertia weight PSO algorithm optimizing SVM kernel parameters applied in a speech recognition system , 2009, 2009 International Conference on Mechatronics and Automation.

[5]  Zenghui Wang,et al.  A new golden ratio local search based particle swarm optimization , 2012, 2012 International Conference on Systems and Informatics (ICSAI2012).

[6]  Aderemi Oluyinka Adewumi,et al.  On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization , 2013, TheScientificWorldJournal.

[7]  M. E. H. Pedersen,et al.  Tuning & simplifying heuristical optimization , 2010 .

[8]  Junying Chen,et al.  Particle Swarm Optimization with Local Search , 2005, 2005 International Conference on Neural Networks and Brain.

[9]  Bo Liu,et al.  Improved particle swarm optimization combined with chaos , 2005 .

[10]  Yong Feng,et al.  Chaotic Inertia Weight in Particle Swarm Optimization , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[11]  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).

[12]  Wei-Bo Zhang,et al.  Study on particle swarm optimization algorithm with local interpolation search , 2010, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).

[13]  M. A. El-Shorbagy,et al.  Local search based hybrid particle swarm optimization algorithm for multiobjective optimization , 2012, Swarm Evol. Comput..

[14]  Xiaolei Han,et al.  Particle Swarm-Simulated Annealing Fusion Algorithm and its Application in Function Optimization , 2008, 2008 International Conference on Computer Science and Software Engineering.

[15]  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.

[16]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[17]  Weerakorn Ongsakul,et al.  A newly improved particle swarm optimization for economic dispatch with valve point loading effects , 2011, 2011 IEEE Power and Energy Society General Meeting.

[18]  K. Hanagaki,et al.  V % Table of Contents , 1988 .

[19]  Xiaojuan Zhao,et al.  Particle swarm optimization using adaptive local search , 2009, 2009 International Conference on Future BioMedical Information Engineering (FBIE).

[20]  M. Montaz Ali,et al.  A comparative study of some real-coded genetic algorithms for unconstrained global optimization , 2011, Optim. Methods Softw..

[21]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[22]  Reza Akbari,et al.  Combination of Particle Swarm Optimization and Stochastic Local Search for Multimodal Function Optimization , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

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

[24]  Aderemi Oluyinka Adewumi,et al.  An Adaptive Velocity Particle Swarm Optimization for high-dimensional function optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[25]  Aderemi Oluyinka Adewumi,et al.  Three new stochastic local search algorithms for continuous optimization problems , 2013, Computational Optimization and Applications.

[26]  Jafar Ememipour,et al.  Introduce a New Inertia Weight for Particle Swarm Optimization , 2009, 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology.

[27]  Yong-Jun Wang,et al.  Improving particle swarm optimization performance with local search for high-dimensional function optimization , 2010, Optim. Methods Softw..