Self-organization of Decentralized Swarm Agents Based on Modified Particle Swarm Algorithm

In this paper, an attempt has been made by incorporating some special features in the conventional particle swarm optimization (PSO) technique for decentralized swarm agents. The modified particle swarm algorithm (MPSA) for the self-organization of decentralized swarm agents is proposed and studied. In the MPSA, the update rule of the best agent in swarm is based on a proportional control concept and the objective value of each agent is evaluated on-line. In this scheme, each agent self-organizes to flock to the best agent in swarm and migrate to a moving target while avoiding collision between the agent and the nearest obstacle/agent. To analyze the dynamics of the MPSA, stability analysis is carried out on the basis of the eigenvalue analysis for the time-varying discrete system. Moreover, a guideline about how to tune the MPSA's parameters is proposed. The simulation results have shown that the proposed scheme effectively constructs a self-organized swarm system in the capability of flocking and migration.

[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  Gary T. Anderson,et al.  Coupled Oscillator Control of Autonomous Mobile Robots , 2000, Auton. Robots.

[3]  Dong Hun Kim A Swarm System Design Based on Coupled Nonlinear Oscillators for Cooperative Behavior , 2003 .

[4]  W. Rappel,et al.  Self-organization in systems of self-propelled particles. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  G. Beni,et al.  The concept of cellular robotic system , 1988, Proceedings IEEE International Symposium on Intelligent Control 1988.

[6]  Aude Billard,et al.  From Animals to Animats , 2004 .

[7]  Gerry Dozier,et al.  Adapting Particle Swarm Optimizationto Dynamic Environments , 2001 .

[8]  G. T. Anderson,et al.  Navigation of autonomous robots with an intelligent oscillator controller , 1999, 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Cat. No.99TH8399).

[9]  Ping Liang,et al.  Robotic morphogenesis , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[10]  Keiichiro Yasuda,et al.  Adaptive particle swarm optimization using velocity information of swarm , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

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

[12]  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).

[13]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[14]  Hirotaka Yoshida,et al.  A PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER AND VOLTAGE CONTROL CONSIDERING VOLTAGE STABILITY , 2000 .

[15]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[16]  Maja J. Mataric,et al.  Broadcast of Local Elibility for Multi-Target Observation , 2000, DARS.

[17]  Yoshikazu Fukuyama,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 2000 .

[18]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[19]  J. Kennedy,et al.  Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[20]  A. Mogilner,et al.  Spatio-angular order in populations of self-aligning objects: formation of oriented patches , 1996 .

[21]  Craig W. Reynolds An evolved, vision-based behavioral model of coordinated group motion , 1993 .

[22]  T. Lubensky,et al.  Response function of a sphere in a viscoelastic two-fluid medium. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Lynne E. Parker,et al.  Cooperative Robotics for Multi-Target Observation , 1999, Intell. Autom. Soft Comput..

[24]  Kai Jin,et al.  Stability of synchronized distributed control of discrete swarm structures , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[25]  Tucker R. Balch,et al.  Behavior-based coordination of large-scale robot formations , 2000, Proceedings Fourth International Conference on MultiAgent Systems.