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.

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.

scholarly journals EVOLUTIONARY ALGORITHM INSPIRED BY THE METHODS OF QUANTUM COMPUTER SCIENCES FOR THE IMPROVEMENT OF A NEURAL MODEL OF THE ELECTRIC POWER EXCHANGE

2017 ◽  
Vol 6 (4) ◽  
pp. 343-355 ◽  
Author(s):  
Jerzy Tchórzewski
Keyword(s):  
Evolutionary Algorithm ◽  
Electric Power ◽  
Quantum Computer ◽  
Electrical Energy ◽  
Neural Model ◽  
Initial Population ◽  
Mutation Operators ◽  
Power Exchange ◽  
Using Data ◽  
Crossover And Mutation

The work contains results of research on the possibility to improve the neural model of the Electric Power Exchange (polish: Towarowa Giełda Energii Elektrycznej – TGEE) in MATLAB and Simulink environment using evolutionary algorithm inspired by quantum computer science. The developed artificial neural network was trained using data for the Day Ahead Market, assuming the joint volume of supplied and sold electrical energy [MWh] as the input quantities in each hour of the 24-hour day, and average prices [PLN/MWh] as output quantities. The obtained model of the exchange system was improved using the evolutionary algorithm, and further improvement in the accuracy of the model by supplementing the evolutionary algorithm using quantum solutions, related to the initial population, crossover and mutation operators, selection, etc. were proposed.

Scheduling Flexible Production Lines with No Intermediate Buffers by Hybrid Algorithms

2012 ◽  
Vol 557-559 ◽  
pp. 2229-2233
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Initial Population ◽  
Flexible Production ◽  
Scheduling Problems ◽  
Production Lines ◽  
Solution Quality ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Better Than

This paper is concerned about the scheduling problems in flexible production lines with no intermediate buffers. The optimization objective is to minimizing the makespan. The mathematical models are presented. Since the problem is NP-hard, a hybrid algorithm, based on genetic algorithm and tabu search, is put forward for solving the models. In this algorithm, the method of generating the initial population is proposed and the crossover and mutation operators, tabu list, and aspiration rule are newly designed. The performance of the hybrid algorithm is compared with that of the traditional genetic algorithm. The computational results show that satisfactory solutions can be obtained by the hybrid algorithm and it performs better than the genetic algorithm in terms of solution quality.

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