scholarly journals Genetic Algorithm for solving flow problems in a Stochastic-flow Network under Budget Constraints

2014 ◽  
Vol 12 (8) ◽  
pp. 3749-3757
Author(s):  
M. R. Hassan
Keyword(s):  
Genetic Algorithm ◽  
Lower Boundary ◽  
Flow Network ◽  
Total Transmission ◽  
Flow Problems ◽  
Boundary Points ◽  
Transmission Cost ◽  
Flow Vector ◽  
Feasible Solutions ◽  
The Given

The system reliability (Rd,c) calculation is based on the generation of all the lower boundary points for the given demand, d, such that the total transmission cost is less than or equal to c (budget). This generation of the lower boundary points is based on finding all feasible solutions to the flow vector, F. The number of feasible solutions cannot be anticipated. Thus, a genetic algorithm is useful for solving this problem. Therefore, this paper presents a genetic algorithm to generate all feasible solutions to the flow vector, F, and then calculate Rd,c. The algorithm is applied to two problem models. In the first, each arc of a flow network has several capacities and may fail. In the second, both the arc and node have several capacities and may fail. The results show that the algorithm is efficient at solving a flow problem and is useful for calculating Rd,c.

scholarly journals Reliability Evaluation for a Stochastic Flow Network Based on Upper and Lower Boundary Vectors

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1115
Author(s):  
Huang ◽  
Huang ◽  
Lin
Keyword(s):  
Lower Boundary ◽  
Reliability Evaluation ◽  
Exclusion Principle ◽  
Stochastic Flow ◽  
Decomposition Approach ◽  
Flow Network ◽  
Boundary Points ◽  
Classic Problem ◽  
Upper Boundary

For stochastic flow network (SFN), given all the lower (or upper) boundary points, the classic problem is to calculate the probability that the capacity vectors are greater than or equal to the lower boundary points (less than or equal to the upper boundary points). However, in some practical cases, SFN reliability would be evaluated between the lower and upper boundary points at the same time. The evaluation of SFN reliability with upper and lower boundary points at the same time is the focus of this paper. Because of intricate relationships among upper and lower boundary points, a decomposition approach is developed to obtain several simplified subsets. SFN reliability is calculated according to these subsets by means of the inclusion-exclusion principle. Two heuristic options are then established in order to calculate SFN reliability in an efficient direction based on the lower and upper boundary points.

Path optimization of CNC PCB drilling using hybrid Taguchi genetic algorithm

Kybernetes ◽  
2016 ◽  
Vol 45 (1) ◽  
pp. 107-125 ◽  
Author(s):  
Dony Hidayat Al-Janan ◽  
Tung-Kuan Liu
Keyword(s):  
Genetic Algorithm ◽  
Printed Circuit Boards ◽  
Numerical Control ◽  
Circuit Boards ◽  
Improved Genetic Algorithm ◽  
Solution Quality ◽  
Content Type ◽  
Neighbor Search ◽  
Feasible Solutions ◽  
The Traveling Salesman Problem

Purpose – In this study, the hybrid Taguchi genetic algorithm (HTGA) was used to optimize the computer numerical control-printed circuit boards drilling path. The optimization was performed by searching for the shortest route for the drilling path. The number of feasible solutions is exponentially related to the number of hole positions. The paper aims to discuss these issues. Design/methodology/approach – Therefore, a traveling cutting tool problem (TCP), which is similar to the traveling salesman problem, was used to evaluate the drilling path; this evaluation is considered an NP-hard problem. In this paper, an improved genetic algorithm embedded in the Taguchi method and a neighbor search method are proposed for improving the solution quality. The classical TCP problems proposed by Lim et al. (2014) were used for validating the performance of the proposed algorithm. Findings – Results showed that the proposed algorithm outperforms a previous study in robustness and convergence speed. Originality/value – The HTGA has not been used for optimizing the drilling path. This study shows that the HTGA can be applied to complex problems.

scholarly journals Economic Environmental Dispatch of Wind Integrated Thermal Power System

2020 ◽  
Vol 8 (6) ◽  
pp. 5186-5192
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Thermal Power ◽  
Nsga Ii ◽  
Strength Pareto Evolutionary Algorithm ◽  
Experimental Constraint ◽  
Thermal Power System ◽  
Power Plant Operation ◽  
Simulation Results ◽  
The Given

In electric power plant operation, Economic Environmental Dispatch (EED) of a thermal-wind is a significant chore to involve allocation of production amongst the running units so the price, NOx extraction status and SO2 extraction status are enhanced concurrently whilst gratifying each and every experimental constraint. This is an exceedingly controlled multiobjective optimizing issue concerning contradictory objectives having Primary and Secondary constraints. For the given work, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is recommended for taking care of EED issue. In simulation results that are obtained by applying the two test systems on the proposed scheme have been evaluated against Strength Pareto Evolutionary Algorithm 2 (SPEA 2).

A Comparative Study of Different Approaches for Finding the Upper Boundary Points in Stochastic-Flow Networks

2014 ◽  
Vol 10 (3) ◽  
pp. 13-23 ◽  
Author(s):  
Seyed Mehdi Mansourzadeh ◽  
Seyed Hadi Nasseri ◽  
Majid Forghani-elahabad ◽  
Ali Ebrahimnejad
Keyword(s):  
Information System ◽  
Comparative Study ◽  
Test Problems ◽  
Stochastic Flow ◽  
Flow Networks ◽  
Flow Network ◽  
Random Test ◽  
Boundary Points ◽  
Exclusion Method ◽  
Upper Boundary

An information system network (ISN) can be modeled as a stochastic-flow network (SFN). There are several algorithms to evaluate reliability of an SFN in terms of Minimal Cuts (MCs). The existing algorithms commonly first find all the upper boundary points (called d-MCs) in an SFN, and then determine the reliability of the network using some approaches such as inclusion-exclusion method, sum of disjoint products, etc. However, most of the algorithms have been compared via complexity results or through one or two benchmark networks. Thus, comparing those algorithms through random test problems can be desired. Here, the authors first state a simple improved algorithm. Then, by generating a number of random test problems and implementing the algorithms in MATLAB, the proposed algorithm is demonstrated to be more efficient than some existing ones in medium-sized networks. The performance profile introduced by Dolan and More is used for analyzing the output of programs.

scholarly journals A GPU-Enabled Compact Genetic Algorithm for Very Large-Scale Optimization Problems

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 758
Author(s):  
Andrea Ferigo ◽  
Giovanni Iacca
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Space ◽  
Large Scale Problems ◽  
Processing Power ◽  
Compact Genetic Algorithm ◽  
Estimation Of Distribution ◽  
Feasible Solutions ◽  
Compact Optimization

The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and material engineering, networked systems, logistics and scheduling. Recently, Deb and Myburgh proposed an evolutionary algorithm capable of handling a scheduling optimization problem with a staggering number of variables: one billion. However, one important limitation of this algorithm is its memory consumption, which is in the order of 120 GB. Here, we follow up on this research by applying to the same problem a GPU-enabled “compact” Genetic Algorithm, i.e., an Estimation of Distribution Algorithm that instead of using an actual population of candidate solutions only requires and adapts a probabilistic model of their distribution in the search space. We also introduce a smart initialization technique and custom operators to guide the search towards feasible solutions. Leveraging the compact optimization concept, we show how such an algorithm can optimize efficiently very large-scale problems with millions of variables, with limited memory and processing power. To complete our analysis, we report the results of the algorithm on very large-scale instances of the OneMax problem.

scholarly journals Propane Pre-Reforming into Methane-Rich Gas over Ni Catalyst: Experiment and Kinetics Elucidation via Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3393 ◽  
Author(s):  
Sergey I. Uskov ◽  
Dmitriy I. Potemkin ◽  
Leniza V. Enikeeva ◽  
Pavel V. Snytnikov ◽  
Irek M. Gubaydullin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Molar Ratio ◽  
Ni Catalyst ◽  
Propane Conversion ◽  
Reaction Conditions ◽  
Experimental Conditions ◽  
Chromium Catalyst ◽  
Nickel Chromium ◽  
Wide Range ◽  
The Given

Pre-reforming of propane was studied over an industrial nickel-chromium catalyst under pressures of 1 and 5 bar, at a low steam to carbon molar ratio of 1, in the temperature range of 220–380 °C and at flow rates of 4000 and 12,000 h−1. It was shown that propane conversion proceeded more efficiently at low pressure (1 atm) and temperatures above 350 °C. A genetic algorithm was applied to search for kinetic parameters better fitting experimental results in such a wide range of experimental conditions. Power law and Langmuir–Hinshelwood kinetics were considered. It was shown that only Langmuir–Hinshelwood type kinetics correctly described the experimental data and could be used to simulate the process of propane pre-reforming and predict propane conversion under the given reaction conditions. The significance of Langmuir–Hinshelwood kinetics increases under high pressure and temperatures below 350 °C.

Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation

2017 ◽  
Vol 6 (2) ◽  
pp. 18-37 ◽  
Author(s):  
Vijaya Lakshmi V. Nadimpalli ◽  
Rajeev Wankar ◽  
Raghavendra Rao Chillarige
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Complex Plane ◽  
Search Space ◽  
Regions Of Interest ◽  
Real Numbers ◽  
Algorithmic Approach ◽  
Space Reduction ◽  
Complex Zeros ◽  
The Given

In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.

Optimization of Wind Farm Micro Sitting Based on Genetic Algorithm

2011 ◽  
Vol 347-353 ◽  
pp. 3545-3550 ◽  
Author(s):  
Chang Xu ◽  
Yan Yan ◽  
De You Liu ◽  
Yuan Zheng ◽  
Chen Qi Li
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Optimal Method ◽  
Successful Search ◽  
Increasing Trend ◽  
The Given

In order to increase wind energy utilization efficiency by the optimization of the wind farm micro sitting, a method which could calculate the wind farm velocity is proposed by consideration of multi turbines wake loss and superposition. Based on the given velocity data of a wind farm, the maximal annual energy production is set as the optimal objective and the ordinates of wind turbines would be the optimal variables, micro sittings of the wind farm turbines are optimized by genetic algorithm. Layout calculation result of the optimal method is quite similar to that of other successful search method, but higher efficiency is reached, and the micro sitting layout is agreement with the regular plum-type layout. Annual energy productions are also calculated under the condition of different wind turbine number. Results show annual energy production increases with the wind turbine number increased, but the increasing trend is lower and lower. The research could provide a reference to wind farm micro-sitting.

CONSTRUCTION OF KERNEL AGGREGATION OPERATORS FROM MARGINAL VALUES

2002 ◽  
Vol 10 (supp01) ◽  
pp. 37-49 ◽  
Author(s):  
A. KOLESÁROVÁ ◽  
E. MUEL ◽  
J. MORDELOVÁ
Keyword(s):  
Aggregation Operators ◽  
Boundary Points ◽  
Marginal Values ◽  
The Given

Binary kernel aggregation operators are studied, especially construction of kernel aggregation operators from the given values at boundary points. Kernel aggregation operators uniquely determined by their marginal values are characterized. The importance of shift invariant aggregation operators and k–medians for construction of such aggregation operators is shown. Several examples are given.

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