Limits...
Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

Cakar T, Koker R - Comput Intell Neurosci (2015)

Bottom Line: PSO searches for better solution than this solution.For each stage, local optimizers are used to perform exploitation to the best particle.All system is named as neurohybrid-PSO solution system.

View Article: PubMed Central - PubMed

Affiliation: Industrial Engineering Department, Engineering Faculty, Sakarya University, Esentepe Campus, 54187 Sakarya, Turkey.

ABSTRACT
A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

No MeSH data available.


PSO algorithm.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4493311&req=5

alg1: PSO algorithm.

Mentions: Particle swarm optimization (PSO) is a population based evolutionary algorithm found by Russell Eberhart and James Kennedy in 1995. This algorithm has been modelled based on the actions of the bird and the fish swarms when they are looking for food and how they are escaping from any dangerous case. Pseudo code of PSO can be seen in Algorithm 1. Since PSO finds solution faster, requires less parameters, and lacks possibility of stopping in local minima, it has superiority on other algorithms.


Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

Cakar T, Koker R - Comput Intell Neurosci (2015)

PSO algorithm.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493311&req=5

alg1: PSO algorithm.
Mentions: Particle swarm optimization (PSO) is a population based evolutionary algorithm found by Russell Eberhart and James Kennedy in 1995. This algorithm has been modelled based on the actions of the bird and the fish swarms when they are looking for food and how they are escaping from any dangerous case. Pseudo code of PSO can be seen in Algorithm 1. Since PSO finds solution faster, requires less parameters, and lacks possibility of stopping in local minima, it has superiority on other algorithms.

Bottom Line: PSO searches for better solution than this solution.For each stage, local optimizers are used to perform exploitation to the best particle.All system is named as neurohybrid-PSO solution system.

View Article: PubMed Central - PubMed

Affiliation: Industrial Engineering Department, Engineering Faculty, Sakarya University, Esentepe Campus, 54187 Sakarya, Turkey.

ABSTRACT
A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

No MeSH data available.