Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
Artificial Intelligence Review
Computers and Operations Research
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Multi-objective query processing for database systems
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Computers & Mathematics with Applications
Hi-index | 0.09 |
In order to solve the multi-objective performance optimal problems, SPEA2+ is used to realize the performance design of injection molding machine. The optimization objectives are constructed to maximize mould control power, maximize injection quantity and minimize injection power. The mathematical model is found to optimize the problem. A solution is extracted to eliminate the imprecise nature of preference through the Pareto optimal set based on fuzzy set theory. Compared with NSGA-II and SPEA2, SPEA2+ could acquire the Pareto front with better distribution and smaller distance with the optimum solutions. Finally, the case illustration of HTG1000X3Y injection molding machine is taken as an example to demonstrate that such method is effective and practical. Effective references could be provided to decision makers for objectives tradeoff at the performance conceptual design stage of injection molding machine.