Title :
An Improved Weight-Based Multiobjective Genetic Algorithm and Its Application to Parallel Machine Scheduling
Author_Institution :
Zhijiang Coll., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
In this study, a weight-based multiobjective genetic algorithm (WBMOGA) is improved. Different from WBMOGA, the modified algorithm presents a novel selection approach based on the truncation algorithm with similar individuals (TASI), and is applied to the parallel machine scheduling in the textile manufacturing industry. Simulation results show that the modified WBMOGA can better solve the parallel machine scheduling problems, and find much better spread of solutions and better convergence near the true Pareto-optimal front compared to the elitist non-dominated sorting genetic algorithm (NSGA-II) and the random weight genetic algorithm (RWGA).
Keywords :
Pareto optimisation; genetic algorithms; single machine scheduling; textile industry; Pareto-optimal front; parallel machine scheduling; selection approach; similar individuals; textile manufacturing industry; truncation algorithm; weight-based multiobjective genetic algorithm; Automation; Concurrent computing; Genetic algorithms; Job shop scheduling; Machine intelligence; Parallel machines; Processor scheduling; Production; Scheduling algorithm; Textiles; evolutionary algorithms; multiobjective; parallel machine; scheduling;
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
DOI :
10.1109/ICICTA.2009.47
