Modified Particle Swarm Optimization for Robust Anti-swing Gantry Crane Controller Tuning

2020 ◽  
pp. 1173-1192
Author(s):  
Mahmud Iwan Solihin ◽  
Wei Hong Lim ◽  
Sew Sun Tiang ◽  
Chun Kit Ang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Gantry Crane ◽  
Controller Tuning ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

Web Service Composition Based on Modified Particle Swarm Optimization

2014 ◽  
Vol 36 (5) ◽  
pp. 1031-1046 ◽  
Author(s):  
Tao WEN ◽  
Guo-Jun SHENG ◽  
Quan GUO ◽  
Ying-Qiu LI
Keyword(s):  
Particle Swarm Optimization ◽  
Web Service ◽  
Service Composition ◽  
Particle Swarm ◽  
Web Service Composition ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

Modified particle swarm optimization with novel population initialization

2019 ◽  
Vol 40 (6) ◽  
pp. 1167-1179 ◽  
Author(s):  
Atieh Khajeh ◽  
Mohammad Reza Ghasemi ◽  
Hamed Ghohani Arab
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization
Sign in / Sign up

Export Citation Format

Share Document