Paper Info
Title
Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem
Abstract
A single-objective optimization problem can be solved more efficiently by introducing some helper-objectives and running a multi-objective evolutionary algorithm. But what objectives should be used at each optimization stage? This paper describes a new method of adaptive helper-objectives selection in multi-objective evolutionary algorithms. The proposed method is applied to the Job-Shop scheduling problem and compared with the previously known approach, which was specially developed for the Job-Shop problem. A comparison with the previously proposed method of adaptive helper-objective selection based on reinforcement learning is performed as well.
Year
DOI
Venue
2013
10.1109/ICMLA.2013.151
ICMLA), 2013 12th International Conference
Keywords
Field
DocType
evolutionary computation,job shop scheduling,learning (artificial intelligence),improved helper objective optimization strategy,job shop scheduling problem,multi objective evolutionary algorithm,reinforcement learning,single objective optimization problem,adaptive selection,helper-objectives,job-shop problem,multi-objective optimization
Memetic algorithm,Mathematical optimization,Job shop scheduling,Evolutionary algorithm,Computer science,Flow shop scheduling,Evolutionary computation,Nurse scheduling problem,Multi-objective optimization,Artificial intelligence,Optimization problem,Machine learning
Conference
Volume
Citations 
PageRank 
2
4
0.48
References 
Authors
7
3
Name
Order
Citations
PageRank
Irina Petrova1162.53
Arina Buzdalova2619.42
Maxim Buzdalov314125.29