scholarly journals Optimizing Painting Sequence Scheduling Based on Adaptive Partheno-Genetic Algorithm

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1714
Author(s):  
Jun Yang ◽  
Tong Sun ◽  
Xiuxiang Huang ◽  
Ke Peng ◽  
Zhongxiang Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Programming Problem ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Real Life ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Rule Based ◽  
Multi Objective ◽  
Color Changes

In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on these restrictions, we construct exact constraints and two objective functions to form a large-scale multi-objective mixed-integer linear programming problem. To reduce this scheduling problem’s complexity, we converted the multi-objective model into a multi-level objective programming problem by combining the rule-based scheduling algorithm and the adaptive Partheno-Genetic algorithm. The rule-based scheduling algorithm is adopted to optimize color changes horizontally and bracket replacements vertically. The adaptive Partheno-Genetic algorithm is designed to optimize production based on the rule-based scheduling algorithm. Finally, we apply the model to the actual optimization problem that contained 829,684 variables and 137,319 constraints, and solved this problem by Python. The proposed method solves the optimal solution, consuming 575 s.

scholarly journals A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing

2020 ◽  
Vol 16 (8) ◽  
pp. 155014772094914
Author(s):  
Muhammad Sardaraz ◽  
Muhammad Tahir
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Scientific Workflows ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Recent Developments ◽  
Benchmark Datasets ◽  
Comparative Results

Recent developments in cloud computing have made it a powerful solution for executing large-scale scientific problems. The complexity of scientific workflows demands efficient utilization of cloud resources to satisfy user requirements. Scheduling of scientific workflows in a cloud environment is a challenge for researchers. The problem is considered as NP-hard. Some constraints such as a heterogeneous environment, dependencies between tasks, quality of service and user deadlines make it difficult for the scheduler to fully utilize available resources. The problem has been extensively studied in the literature. Different researchers have targeted different parameters. This article presents a multi-objective scheduling algorithm for scheduling scientific workflows in cloud computing. The solution is based on genetic algorithm that targets makespan, monetary cost, and load balance. The proposed algorithm first finds the best solution for each parameter. Based on these solutions, the algorithm finds the superbest solution for all parameters. The proposed algorithm is evaluated with benchmark datasets and comparative results with the standard genetic algorithm, particle swarm optimization, and specialized scheduler are presented. The results show that the proposed algorithm achieves an improvement in makespan and reduces the cost with a well load balanced system.

Grid Task Scheduling Algorithm Based on Non-Conflict Degree

2012 ◽  
Vol 239-240 ◽  
pp. 1497-1500
Author(s):  
Fu Qiao ◽  
Yan Juan Zhang ◽  
Ze Yuan Gu ◽  
Juan Wang ◽  
Guo Yin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Solution Procedure ◽  
Multi Objective ◽  
Task Scheduling Algorithm ◽  
Conflict Problem ◽  
Tasks Scheduling ◽  
Better Than

To solve the multi-objective conflict problem of independent task scheduling in grid computing, model the system along the direction of multi-objective gradient, give the model solution procedure, propose grid scheduling algorithm based on multi-objective non-conflict degree, that is, application of gradient ascent method does a quadratic optimization for the global optimal solution obtained by genetic algorithm, compensate for the GA (genetic algorithm) deficiency in the ability of local search. The experiments demonstrate that the proposed algorithm is better than other tasks scheduling algorithms simply using GA.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm

2022 ◽  
pp. 014233122110667
Author(s):  
Tufan Dogruer ◽  
Mehmet Serhat Can
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Pid Controller ◽  
Robustness Analysis ◽  
Optimal Solution ◽  
Voltage Regulator ◽  
Fuzzy Pid ◽  
Multi Objective ◽  
Fuzzy Pid Controller ◽  
Automatic Voltage Regulator

In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

2019 ◽  
Vol 233 (10) ◽  
pp. 1282-1297 ◽  
Author(s):  
Binghai Zhou ◽  
Wenlong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Processing Time ◽  
Flow Shop ◽  
Scheduling Algorithm ◽  
Mixed Integer ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Multi Objective ◽  
Fuzzy Processing Time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

scholarly journals Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing

2020 ◽  
Vol 12 (6) ◽  
pp. 2177
Author(s):  
Jun-Ho Huh ◽  
Jimin Hwa ◽  
Yeong-Seok Seo
Keyword(s):  
Genetic Algorithm ◽  
Software Architecture ◽  
Large Scale ◽  
Structural Characteristics ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Software Systems ◽  
Small Scale ◽  
Hill Climbing Algorithm ◽  
Architecture Management

A Hierarchical Subsystem Decomposition (HSD) is of great help in understanding large-scale software systems from the software architecture level. However, due to the lack of software architecture management, HSD documentations are often outdated, or they disappear in the course of repeated changes of a software system. Thus, in this paper, we propose a new approach for recovering HSD according to the intended design criteria based on a genetic algorithm to find an optimal solution. Experiments are performed to evaluate the proposed approach using two open source software systems with the 14 fitness functions of the genetic algorithm (GA). The HSDs recovered by our approach have different structural characteristics according to objectives. In the analysis on our GA operators, crossover contributes to a relatively large improvement in the early phase of a search. Mutation renders small-scale improvement in the whole search. Our GA is compared with a Hill-Climbing algorithm (HC) implemented by our GA operators. Although it is still in the primitive stage, our GA leads to higher-quality HSDs than HC. The experimental results indicate that the proposed approach delivers better performance than the existing approach.

An Improved Cloud Adaptive Genetic Algorithm Based on Cloud Computing for Active Optimization Calculation

2015 ◽  
Vol 713-715 ◽  
pp. 1579-1582
Author(s):  
Shao Min Zhang ◽  
Ze Wu ◽  
Bao Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Large Scale ◽  
Optimal Solution ◽  
Adaptive Genetic Algorithm ◽  
Load Optimization ◽  
Active Load ◽  
Optimization Calculation ◽  
Local Optimal Solution ◽  
Optimization Allocation

Under the background of huge amounts of data in large-scale power grid, the active power optimization calculation is easy to fall into local optimal solution, and meanwhile the calculation demands a higher processing speed. Aiming at these questions, the farmer fishing algorithm which is applied to solve the problem of optimal distribution of active load for coal-fired power units is used to improve the cloud adaptive genetic algorithm (CAGA) for speeding up the convergence phase of CAGA. The concept of cloud computing algorithm is introduced, and parallel design has been done through MapReduce graphs. This method speeds up the calculation and improves the effectiveness of the active load optimization allocation calculation.

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