Titlebook: Advances in Swarm Intelligence; 4th International Co Ying Tan,Yuhui Shi,Hongwei Mo Conference proceedings 2013 Springer-Verlag Berlin Heide

CLOUT

https://doi.org/10.1007/978-3-540-36856-4timization (PSO) algorithm, where the choice of the parameters is inspired by [4], in order to avoid diverging trajectories of the particles, and help the exploration of the feasible set. Moreover, we extend the ideas in [4] and propose a specific set of initial particles position for the bound constrained problem.

碎石

Opposition-Based Learning Fully Informed Particle Swarm Optimizer without Velocityorithm is the simpler and more effective. The proposed algorithm is applied to some well-known benchmarks. The relative experimental results show that the algorithm achieves better solutions and faster convergence.

博愛(ài)家

GSO: An Improved PSO Based on Geese Flight Theoryity. Moreover, the rules and hypotheses for formation flight adhere to all five basic principles of swarm intelligence. Therefore, the proposed geese-flight theory is highly rational and has important theoretical innovations, and GSO algorithm can be utilized in a wide range of applications.

Torrid

Picks-Disease

Maturity of the Particle Swarm as a Metric for Measuring the Collective Intelligence of the Swarmecause of the lack of the system’s awareness, and that a solution would be some adaptation of particle’s behavioural rules so that the particle could adjust its velocity using control parameters whose value would be derived from inside of the swarm system, without tuning.

Obsequious

偶像

Interactive Robotic Fish for the Analysis of Swarm Behavioran execute certain behaviors integrating feedback from the swarm’s position, orientation and velocity. Here, we describe implementation details of our hardware and software and show first results of the analysis of behavioral experiments.

Muscularis

Particle Swarm Optimization in Regression Analysis: A Case Studyto obtain the minimum sum of absolute difference values between observed data points and calculated data points by the regression function. Experimental results show that particle swarm optimization can obtain good performance on regression analysis problems.

nocturia

Mechanical PSO Aided by Extremum Seeking for Swarm Robots Cooperative Searchhe ES based method is capable of driving robots to the purposed states generated by mechanical PSO without the necessity of robot localization. By this way, the whole robot swarm approaches the searched target cooperatively. This pilot study is verified by numerical experiments in which different robot sensors are mimicked.

Neolithic

Multi-swarm Particle Swarm Optimization with a Center Learning Strategye center position of its own swarm. Experiments are conducted on five test functions to compare with some variants of the PSO. Comparative results on five benchmark functions demonstrate that MPSOCL achieves better performances in both the optimum achieved and convergence performance than other algorithms generally.