Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm

2004 ◽  
Author(s):  
K. K. Botros ◽  
D. Sennhauser ◽  
K. J. Jungowski ◽  
G. Poissant ◽  
H. Golshan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Pipeline ◽  
Multi Objective Optimization ◽  
Maximum Throughput ◽  
Multi Objective ◽  
Decision Variables ◽  
Pipeline Networks ◽  
Multiple Unit ◽  
Pareto Fronts ◽  
Selection Of

This paper presents application of Genetic Algorithm (GA) methodologies to multi-objective optimization of two complex gas pipeline networks to achieve specific operational objectives. The first network contains 10 compressor stations resulting in 20 decision variables and an optimization space of 6.3 × 1029 cases. The second system contains 25 compressor stations resulting in 54 decision variables and an optimization space of 1.85 × 1078 cases. Compressor stations generally included multiple unit sites, where the compressor characteristics of each unit is taken into account constraining the solution by the surge and stonewall limits, maximum and minimum speeds and maximum power available. A key challenge to the optimization of such large systems is the number of constraints and associated penalty functions, selection of the GA operators such as crossover, mutation, selection criteria and elitism, as well as the population size and number of generations. The paper discusses the approach taken to arrive at optimal values for these parameters for large gas pipeline networks. Examples for two-objective optimizations, referred to as Pareto fronts, include maximum throughput and minimum fuel, as well as, minimum linepack and maximum throughput in typical linepack/throughput/fuel envelopes.

scholarly journals Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

2019 ◽  
Vol 4 (2) ◽  
pp. 35 ◽  
Author(s):  
Ulrich A. Ngamalieu-Nengoue ◽  
Pedro L. Iglesias-Rey ◽  
F. Javier Martínez-Solano
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Urban Drainage ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Space Reduction ◽  
Decision Variables ◽  
Drainage Networks ◽  
Search Space Reduction ◽  
Pareto Fronts

The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING NSGA-II ALGORITHM

2016 ◽  
Vol 8 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Mehdi Babaei ◽  
Masoud Mollayi
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Steel Structures ◽  
American Concrete Institute ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Pareto Fronts

In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concrete Institute (ACI) are employed as the design constraints. Pareto-fronts for the objective space have been obtained for RC frame models of four, eight and twelve floors. The results indicate smooth Pareto-fronts and prove the speed and accuracy of the method.

Micro-Genetic algorithm with fuzzy selection of operators for multi-Objective optimization: μFAME

2021 ◽  
Vol 61 ◽  
pp. 100818
Author(s):  
Alejandro Santiago ◽  
Bernabé Dorronsoro ◽  
Héctor J. Fraire ◽  
Patricia Ruiz
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Micro Genetic Algorithm ◽  
Selection Of

Large Pipeline Network Optimization: Summary and Conclusions of TransCanada Research Effort

2006 ◽  
Author(s):  
K. K. Botros ◽  
D. Sennhauser ◽  
J. Stoffregen ◽  
K. J. Jungowski ◽  
H. Golshan
Keyword(s):  
Genetic Algorithm ◽  
Research Effort ◽  
Gas Pipeline ◽  
Stochastic Methods ◽  
Pipeline System ◽  
Operational Experience ◽  
Pipeline Network ◽  
Multi Objective ◽  
The Past ◽  
Decision Variables

Operation of large gas pipeline networks calls for fulfilling variation in contractual volume obligations, and maintaining a certain range of linepack with minimum fuel consumptions to drive compressor units. This is often achieved with either operational experience or by utilization of optimization tools, which results in reduced hydraulic analysis time as well as improved pipeline operation as a whole. The main objective is to accurately identify the optimum set points for all compressor stations, control and block valves in the network, subject to several system and operational constraints. This implies multi-objective optimization of a highly constrained network with a large number of decision variables. Over the past three years, TransCanada has devoted a research effort in developing/integrating an optimization tool based on stochastic methods. It was found that it offers greater stability and is more suited for multi-objective optimizations of large networks with inherently large number of decision variables, than any gradient-based method. This paper describes the nature of the pipeline system under optimization, and discusses the basis for a Genetic-Algorithm-based tool employed. It summarizes the results of the past three years of research efforts outlining the selection criteria for the optimization parameters, integration with a robust steady-state thermal hydraulic simulator of the pipeline network and the notion that dynamic penalty parameters can affect convergence. The methodology is applied to a large gas pipeline network containing 22 compressor stations resulting in 54 decision variables and an optimization space of 1.85×1078 cases. Comparison of genetic algorithm optimization with traditional and manual optimization is demonstrated. Extensive effort has been devoted to reduce the computation time, which includes techniques to utilize various hybrid surrogate methods such as Kriging, Neural Networks, Response Surface, as well as exploitation of parallel processing.

MULTI-OBJECTIVE OPTIMIZATION OF OFFSHORE TSUNAMI OBSERVATION POINTS BY GENETIC ALGORITHM

2020 ◽  
Vol 76 (2) ◽  
pp. I_325-I_330
Author(s):  
Kazutoshi KURAMOTO ◽  
Fumiyasu MAKINOSHIMA ◽  
Anawat SUPPASRI ◽  
Fumihiko IMAMURA
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective
Sign in / Sign up

Export Citation Format

Share Document