2005
DOI: 10.1007/11539902_70
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Discrete Particle Swarm Optimization (DPSO) Algorithm for Permutation Flowshop Scheduling to Minimize Makespan

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Cited by 67 publications
(36 citation statements)
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“…Best fitness encountered by each particle (local best) is stored and the information is shared with other particles to obtain the best particle (global best). In this paper, the velocity and position update of PSO are based on [36] to suit the presented UAV scheduling problem.…”
Section: Application Of Pso For Uav Scheduling Systemmentioning
confidence: 99%
See 2 more Smart Citations
“…Best fitness encountered by each particle (local best) is stored and the information is shared with other particles to obtain the best particle (global best). In this paper, the velocity and position update of PSO are based on [36] to suit the presented UAV scheduling problem.…”
Section: Application Of Pso For Uav Scheduling Systemmentioning
confidence: 99%
“…Different operators used in computing the particle velocity and position are explained as follows [36]. …”
Section: Algorithm 1 Particle Swarm Optimization Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…Afterwards the PSO-BB-BCA algorithm was applied DNR problem in the presence of capacitor placement [13] whereas; artificial bee colony (ABC) algorithm has been employed to minimize distribution power loss minimization in the existence of distributed generation and capacitor [14]. Rameshkumar et al, has applied discrete particle swarm optimization (DPSO) algorithm for solving permutation flow shop scheduling problems with the objective of minimizing the make-span [15]. Followed, Mingyu Li et al, has proposed an improved discrete particle swarm optimization (IDPSO) algorithm for high-speed train assembly sequence planning where the particles have a larger probability learning coefficients that improves the convergence rate [16].…”
Section: Introductionmentioning
confidence: 99%
“…As, [15] and [16] proven its ability for finding good quality of solution it is motivated to optimize the above stated control variables, but the larger learning coefficients might tune the exploration ability of the algorithm that has improved convergence rate only. Therefore, in this paper an adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for finding feasible solutions to DNR problem.…”
Section: Introductionmentioning
confidence: 99%