A genetic algorithm for the vehicle routing problem
Computers and Operations Research
Design and Analysis of Experiments
Design and Analysis of Experiments
Journal of Systems and Software
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Swarm optimized organizing map (SWOM): A swarm intelligence basedoptimization of self-organizing map
Expert Systems with Applications: An International Journal
Efficient population utilization strategy for particle swarm optimizer
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Hi-index | 12.05 |
This study minimizes the periodic preventive maintenance cost for a series-parallel system using an improved particle swam optimization (IPSO). The optimal maintenance periods for all components in the system are determined efficiently. Though having advantages such as simple understanding and easy operation, a typical particle swam optimization (PSO) is easily trapped in local solutions when optimizing complex problems and yields inferior solutions. The proposed IPSO considers maintainable properties of a series-parallel system. The importance measure of components is utilized to evaluate the effects of components on system reliability when maintaining a component. Accordingly, the important components form superior initial particles. Furthermore, an adjustment mechanism is developed to deal with the problem in which particles move into an infeasible area. A replacement mechanism is implemented that replaces the first n particles ranked in descending order of total maintenance cost with randomly generated particles in the feasible area. The purpose of doing so is overcome the weakness in that a typical PSO is easily trapped in local solutions when optimizing a complex problem. An elitist strategy is also applied within the IPSO. Additionally, this study employs response surface methodology (RSM) via systematic parameters experiments to determine the optimal settings of IPSO parameters. Finally, a case demonstrates the effectiveness of the proposed IPSO in optimizing the periodic preventive maintenance model for series-parallel systems.