Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Discovering Statistics Using SPSS
Discovering Statistics Using SPSS
An architecture for workflow scheduling under resource allocation constraints
Information Systems
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Quality Assessment of Pareto Set Approximations
Multiobjective Optimization
Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling
Computers and Operations Research
Parallel machine scheduling with precedence constraints and setup times
Computers and Operations Research
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Size-reduction heuristics for the unrelated parallel machines scheduling problem
Computers and Operations Research
Computers and Operations Research
Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud
IEEE Transactions on Parallel and Distributed Systems
Computers and Operations Research
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Solving the job-shop scheduling problem optimally by dynamic programming
Computers and Operations Research
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
Purpose: The objective of this study is to optimize task scheduling and resource allocation using an improved differential evolution algorithm (IDEA) based on the proposed cost and time models on cloud computing environment. Methods: The proposed IDEA combines the Taguchi method and a differential evolution algorithm (DEA). The DEA has a powerful global exploration capability on macro-space and uses fewer control parameters. The systematic reasoning ability of the Taguchi method is used to exploit the better individuals on micro-space to be potential offspring. Therefore, the proposed IDEA is well enhanced and balanced on exploration and exploitation. The proposed cost model includes the processing and receiving cost. In addition, the time model incorporates receiving, processing, and waiting time. The multi-objective optimization approach, which is the non-dominated sorting technique, not with normalized single-objective method, is applied to find the Pareto front of total cost and makespan. Results: In the five-task five-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.368 and C(IDEA, NSGA-II) of 0.3 are superior to the ratios C(DEA, IDEA) of 0.249 and C(NSGA-II, IDEA) of 0.288, respectively. In the ten-task ten-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.506 and C(IDEA, NSGA-II) of 0.701 are superior to the ratios C(DEA, IDEA) of 0.286 and C(NSGA-II, IDEA) of 0.052, respectively. Wilcoxon matched-pairs signed-rank test confirms there is a significant difference between IDEA and the other methods. In summary, the above experimental results confirm that the IDEA outperforms both the DEA and NSGA-II in finding the better Pareto-optimal solutions. Conclusions: In the study, the IDEA shows its effectiveness to optimize task scheduling and resource allocation compared with both the DEA and the NSGA-II. Moreover, for decision makers, the Gantt charts of task scheduling in terms of having smaller makespan, cost, and both can be selected to make their decision when conflicting objectives are present.