CLOUT
发表于 2025-3-25 06:30:15
https://doi.org/10.1007/978-3-540-36856-4timization (PSO) algorithm, where the choice of the parameters is inspired by , in order to avoid diverging trajectories of the particles, and help the exploration of the feasible set. Moreover, we extend the ideas in and propose a specific set of initial particles position for the bound constrained problem.
碎石
发表于 2025-3-25 08:46:04
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.
博爱家
发表于 2025-3-25 15:26:03
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
发表于 2025-3-25 19:19:17
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Picks-Disease
发表于 2025-3-25 20:40:09
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
发表于 2025-3-26 01:59:24
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偶像
发表于 2025-3-26 08:08:12
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
发表于 2025-3-26 09:59:51
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
发表于 2025-3-26 16:18:24
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
发表于 2025-3-26 20:24:51
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.