EFFECTS OF THE DIFFERENT MIGRATION PERIODS ON PARALLEL MULTI -SWARM PSO

In recent years, there has been an increasing interest in parallel computing. In parallel computing, multiple computing resources are used simultaneously in solving a problem. There are multiple processors that will work concurrently and the program is divided into different tasks to be simultaneously solved. Recently, a considerable literature has grown up around the theme of metaheuristic algorithms. Particle swarm optimization (PSO) algorithm is a popular metaheuristic algorithm. The parallel comprehensive learning particle swarm optimization (PCLPSO) algorithm based on PSO has multiple swarms based on the master-slave paradigm and works cooperatively and concurrently. The migration period is an important parameter in PCLPSO and affects the efficiency of the algorithm. We used the well-known benchmark functions in the experiments and analysed the performance of PCLPSO using different migration periods.

[1]  Xiaodong Li,et al.  Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology , 2010, IEEE Transactions on Evolutionary Computation.

[2]  Halife Kodaz,et al.  A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization , 2015, Eng. Appl. Artif. Intell..

[3]  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).

[4]  Carlos A. Coello Coello,et al.  On the use of particle swarm optimization with multimodal functions , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[5]  El-Ghazali Talbi,et al.  New Results - A Parallel Bi-objective Hybrid Metaheuristic for Energy-Aware Scheduling for Cloud Computing Systems , 2011 .

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

[7]  Bo Li,et al.  Communication Latency Tolerant Parallel Algorithm for Particle Swarm Optimization , 2009, 2009 Fourth International Conference on Frontier of Computer Science and Technology.

[8]  Ponnuthurai N. Suganthan,et al.  A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[9]  Martín Pedemonte,et al.  A survey on parallel ant colony optimization , 2011, Appl. Soft Comput..

[10]  Albert Y. Zomaya,et al.  Author manuscript, published in "Journal of Parallel and Distributed Computing (2011)" A Parallel Bi-objective Hybrid Metaheuristic for Energy-aware Scheduling for Cloud Computing Systems , 2011 .

[11]  Agostino Poggi,et al.  Developing Multi-agent Systems with JADE , 2007, ATAL.

[12]  Jun Zhang,et al.  Parallel Particle Swarm Optimization Using Message Passing Interface , 2015 .

[13]  Zhipeng Guo,et al.  An implicit parallel multigrid computing scheme to solve coupled thermal-solute phase-field equations for dendrite evolution , 2012, J. Comput. Phys..

[14]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[15]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  Jun Pang,et al.  Modeling and simulation of a nanoscale optical computing system , 2014, J. Parallel Distributed Comput..

[17]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[18]  Enrique Alba,et al.  Parallel Metaheuristics: A New Class of Algorithms , 2005 .