Particle Swarm Optimization Algorithm for Permutation Flowshop Sequencing Problem

Taşgetiren, M. Fatih; Sevkli, Mehmet; Liang, Yun-Chia; Gençyılmaz, Güneş

doi:10.1007/978-3-540-28646-2_38

Cited by 137 publications

(90 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PSO was first used for discrete problem [26] in 1997; the first article discussing the application of PSO to solving PFSP was presented by Tasgetiren et al [27] [36].The main idea is that each individual updates its current solution to reference with its own history experiences and the experiences of others.Base on [25] and [37], each particle is generated using a random solution. The goal for each particle is to search for solution space and update its own solution between cognitive behaviour and social behaviour in the swarm, as the following Eqs.5 and 6.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Two-Phase Hybrid Particle Swarm Optimization Algorithm for Solving Permutation Flow-Shop Scheduling Problem

Huang¹,

Yang²,

Tsai³

2012

IJCA

View full text Add to dashboard Cite

In this paper, a two-phase hybrid particle swarm optimization algorithm (PRHPSO) is proposed for the permutation flowshop scheduling problem (PFSP) with the minimizing makespan measure. The smallest position value (SPV) rule is used for encoding the particles that enable PSO for suitable PFSP, and the NEH and Tabu search algorithms are used for initializing the particles. In the first phase, the pattern reduction (PR) operator is used in the PSO algorithm for reducing the computation time. In order to avoid a premature convergence, a regeneration operator is used for escaping to the local optimal and balancing exploitation and exploration in the second phase. Additionally, a simulated annealing (SA) algorithm is utilized for local search to improve the best solution after the PSO search process. Finally, the results show that PRHPSO is significantly faster than two PSO-based algorithms and presents large-sized benchmarks. General TermsHybrid Metaheuristic Algorithm, Combinatorial optimization.

show abstract

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Two-Phase Hybrid Particle Swarm Optimization Algorithm for Solving Permutation Flow-Shop Scheduling Problem

Huang¹,

Yang²,

Tsai³

2012

IJCA

View full text Add to dashboard Cite

show abstract

“…A particle moved according to a probability distribution function determined using the Hamming distance between two points in the binary space. The early concepts introduced by the binary PSO appeared in later PSO algorithms for combinatorial optimisation such as in (Shi et al, 2006); (Tasgetiren et al, 2004); (Liu et al, 2007b); (Pang et al, 2004); (Martínez García and Moreno Pérez, 2008); (Song et al, 2008); and (Wang et al, 2003). Tasgetiren et al (Tasgetiren et al, 2004) introduced the smallest position value rule (SPV) to enable the continuous PSO algorithm to be applied the class of sequencing and combinatorial problems.…”

Section: Related Workmentioning

confidence: 99%

“…At each iteration, the position value is updated according to the traditional velocity update equation and the sequence of objects is re-sorted according to the values assigned to the continuous space. The method proposed by (Tasgetiren et al, 2004) is similar to the random keys in GA (Bean, 1994). Following a similar method to (Kennedy and Eberhart, 1997), Wang et al (Wang et al, 2003) introduced the concept of a swap operator to exchange dimensions in the particle position.…”

Section: Related Workmentioning

confidence: 99%

A Particle Swarm Optimisation Approach to Graph Permutations

Ilaya¹,

Bil²

2009

Particle Swarm Optimization

View full text Add to dashboard Cite

“…Lee and Park (20006) studied the relative merits and de-merits of applying particle swarm optimization technique to economic dispatch problem. Tasgetiren and Liang (2003) implemented the binary particle swarm optimization technique to determine the economic lot size. Kumar et al (2008) presented in detail, the basic concepts of particle swarm optimization, its variants and its application with reference to power systems.…”

Section: Introductionmentioning

confidence: 99%

Work space optimization of a r-r planar manipulator using particle swarm optimization technique

Chaitanya¹,

Krishna²

2017

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

A two link revolute planar robotic manipulator is optimized for maximization of work space covered by its end effector. A mathematical model for optimization is built considering singularities which control the range of design variables. Condition number which is the measure of change in output value (End effector position) for a small change in input value (joint angles) is modeled as the constraint. The non linear optimization model is initially linearised using Sequential Linear Programming technique and is solved graphically for optimum value of objective function. Particle swarm optimization technique is implemented on the non-linear optimization model for optimum value of objective function. The maximum value of objective function obtained from Particle swarm technique is found to be significantly higher than the value obtained using graphical approach.

show abstract

Particle Swarm Optimization Algorithm for Permutation Flowshop Sequencing Problem

Cited by 137 publications

References 15 publications

A Two-Phase Hybrid Particle Swarm Optimization Algorithm for Solving Permutation Flow-Shop Scheduling Problem

A Two-Phase Hybrid Particle Swarm Optimization Algorithm for Solving Permutation Flow-Shop Scheduling Problem

A Particle Swarm Optimisation Approach to Graph Permutations

Work space optimization of a r-r planar manipulator using particle swarm optimization technique

Contact Info

Product

Resources

About