Multicriteria Decision Mechanism CNSGA-AHP for the Automatic Test Task Scheduling Problem

2012 ◽  
Vol 11 (10) ◽  
pp. 1449-1455
Author(s):  
Hui Lu ◽  
Ruiyao Niu ◽  
Jing Liu ◽  
Zheng Zhu
Keyword(s):  
Task Scheduling ◽  
Scheduling Problem ◽  
Automatic Test ◽  
Test Task ◽  
Multicriteria Decision ◽  
Decision Mechanism

scholarly journals A Variable Neighborhood MOEA/D for Multiobjective Test Task Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Hui Lu ◽  
Zheng Zhu ◽  
Xiaoteng Wang ◽  
Lijuan Yin
Keyword(s):  
Task Scheduling ◽  
Search Space ◽  
Global Optimum ◽  
Scheduling Problem ◽  
Local Optima ◽  
Test Task ◽  
Combinational Optimization ◽  
The Mean ◽  
Nondominated Sorting ◽  
Better Than

Test task scheduling problem (TTSP) is a typical combinational optimization scheduling problem. This paper proposes a variable neighborhood MOEA/D (VNM) to solve the multiobjective TTSP. Two minimization objectives, the maximal completion time (makespan) and the mean workload, are considered together. In order to make solutions obtained more close to the real Pareto Front, variable neighborhood strategy is adopted. Variable neighborhood approach is proposed to render the crossover span reasonable. Additionally, because the search space of the TTSP is so large that many duplicate solutions and local optima will exist, the Starting Mutation is applied to prevent solutions from becoming trapped in local optima. It is proved that the solutions got by VNM can converge to the global optimum by using Markov Chain and Transition Matrix, respectively. The experiments of comparisons of VNM, MOEA/D, and CNSGA (chaotic nondominated sorting genetic algorithm) indicate that VNM performs better than the MOEA/D and the CNSGA in solving the TTSP. The results demonstrate that proposed algorithm VNM is an efficient approach to solve the multiobjective TTSP.

Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem

2013 ◽  
Vol 18 (12) ◽  
pp. 2385-2396 ◽  
Author(s):  
Hui Lu ◽  
Jing Liu ◽  
Ruiyao Niu ◽  
Zheng Zhu
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Scheduling Problem ◽  
Parallel Genetic Algorithm ◽  
Test Task ◽  
Distance Analysis

Constraint Handling Technique in Test Task Scheduling Problem

2014 ◽  
Vol 13 (8) ◽  
pp. 1495-1504
Author(s):  
Hui Lu ◽  
Xiaoteng Wang ◽  
Jing Liu
Keyword(s):  
Task Scheduling ◽  
Constraint Handling ◽  
Scheduling Problem ◽  
Test Task ◽  
Handling Technique

scholarly journals Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-25
Author(s):  
Hui Lu ◽  
Lijuan Yin ◽  
Xiaoteng Wang ◽  
Mengmeng Zhang ◽  
Kefei Mao
Keyword(s):  
Evolutionary Algorithm ◽  
Task Scheduling ◽  
Chaotic Maps ◽  
Initial Population ◽  
Scheduling Problem ◽  
Local Optima ◽  
Test Task ◽  
Multiobjective Evolutionary Algorithm ◽  
Mutation Operators ◽  
Crossover And Mutation

Test task scheduling problem (TTSP) is a complex optimization problem and has many local optima. In this paper, a hybrid chaotic multiobjective evolutionary algorithm based on decomposition (CMOEA/D) is presented to avoid becoming trapped in local optima and to obtain high quality solutions. First, we propose an improving integrated encoding scheme (IES) to increase the efficiency. Then ten chaotic maps are applied into the multiobjective evolutionary algorithm based on decomposition (MOEA/D) in three phases, that is, initial population and crossover and mutation operators. To identify a good approach for hybrid MOEA/D and chaos and indicate the effectiveness of the improving IES several experiments are performed. The Pareto front and the statistical results demonstrate that different chaotic maps in different phases have different effects for solving the TTSP especially the circle map and ICMIC map. The similarity degree of distribution between chaotic maps and the problem is a very essential factor for the application of chaotic maps. In addition, the experiments of comparisons of CMOEA/D and variable neighborhood MOEA/D (VNM) indicate that our algorithm has the best performance in solving the TTSP.

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