scholarly journals A Genetic Algorithm Based Hybrid Approach for Reliability-Redundancy Optimization Problem of a Series System with Multiple-Choice

2017 ◽  
Vol 2 (3) ◽  
pp. 185-212 ◽  
Author(s):  
Asoke Kumar Bhunia ◽  
Avijit Duary ◽  
Laxminarayan Sahoo
Keyword(s):  
Genetic Algorithm ◽  
System Reliability ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Multiple Choice ◽  
Function Technique ◽  
Series System ◽  
Special Cases ◽  
Real Coded Genetic Algorithm

The goal of this paper is to introduce an application of hybrid algorithm in reliability optimization problems for a series system with parallel redundancy and multiple choice constraints to maximize the system reliability subject to system budget and also to minimize the system cost subject to minimum level of system reliability. Both the problems are solved by using penalty function technique for dealing with the constraints and hybrid algorithm. In this algorithm, the well-known real coded Genetic Algorithm is combined with Self-Organizing Migrating Algorithm. As special cases, both the problems are formulated and solved considering single component without redundancy. Finally, the proposed approach is illustrated by some numerical examples and the computational results are discussed.

SPLIT AND DISCARD STRATEGY: A NEW APPROACH FOR CONSTRAINED GLOBAL OPTIMIZATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350023
Author(s):  
SYEDA DARAKHSHAN JABEEN
Keyword(s):  
Constrained Optimization ◽  
Penalty Function ◽  
Hybrid Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Function Technique ◽  
Test Problems ◽  
Double Mutation ◽  
Fitness Value ◽  
Real Coded Genetic Algorithm

In this paper we develop a new hybrid algorithm incorporating the penalty function technique for solving nonlinear constrained optimization problems. The principle is based on converting the constrained optimization problem into an unconstrained optimization problem by the penalty function technique. Then, we have proposed a new penalty technique, called Big-M penalty that is different from the existing ones. Accordingly, a hybrid algorithm has been developed based on Split and Discard Strategy (SDS) and advanced real coded genetic algorithm (ARCGA), with tournament selection, multiparent whole arithmetical crossover, double mutation (boundary and whole nonuniform mutation) and elitism. In SDS technique, the entire search space is divided into two equal subregions. Then the one containing the feasible solution with better fitness value is selected. This process is repeated until the accepted subregion reduces to a very small region with negligible edges. Finally, to test the performance of the proposed method along with three different penalty function techniques, it is applied to several well-known benchmark test problems available in the literature.

scholarly journals A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Andrea Villagra ◽  
Enrique Alba ◽  
Guillermo Leguizamón
Keyword(s):  
Genetic Algorithm ◽  
Ad Hoc ◽  
Optimization Problems ◽  
Scatter Search ◽  
Hybrid Approach ◽  
Active Components ◽  
New Techniques ◽  
The Past ◽  
Cellular Genetic Algorithm ◽  
Optimization Search

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

scholarly journals Improvement Analysis and Application of Real-Coded Genetic Algorithm for Solving Constrained Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jiquan Wang ◽  
Zhiwen Cheng ◽  
Okan K. Ersoy ◽  
Panli Zhang ◽  
Weiting Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Global Search ◽  
Single Mutation ◽  
Constrained Optimization Problems ◽  
Fitness Value ◽  
Steering Mechanism ◽  
Real Coded Genetic Algorithm

An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization problems. First, a sorting grouping selection method is given with the advantage of easy realization and not needing to calculate the fitness value. Secondly, a heuristic normal distribution crossover (HNDX) operator is proposed. It can guarantee the cross-generated offsprings to locate closer to the better one among the two parents and the crossover direction to be very close to the optimal crossover direction or to be consistent with the optimal crossover direction. In this way, HNDX can ensure that there is a great chance of generating better offsprings. Thirdly, since the GA in the existing literature has many iterations, the same individuals are likely to appear in the population, thereby making the diversity of the population worse. In IRCGA, substitution operation is added after the crossover operation so that the population does not have the same individuals, and the diversity of the population is rich, thereby helping avoid premature convergence. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, this paper proposes a combinational mutation method, which makes the mutation operation take into account both local search and global search. The computational results with nine examples show that the IRCGA has fast convergence speed. As an example application, the optimization model of the steering mechanism of vehicles is formulated and the IRCGA is used to optimize the parameters of the steering trapezoidal mechanism of three vehicle types, with better results than the other methods used.

Redundancy optimization for multi-state series-parallel systems using ordinal optimization-based-genetic algorithm

2021 ◽  
pp. 1748006X2110286
Author(s):  
Yishuang Hu ◽  
Yi Ding ◽  
Zhiguo Zeng
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Real Life ◽  
Parallel Systems ◽  
Function Technique ◽  
Ordinal Optimization ◽  
Engineering Systems ◽  
Designing Method ◽  
Redundancy Optimization

Multi-state series-parallel systems (MSSPSs) are widely-used for representing engineering systems. In real-life cases, engineers need to design an optimal MSSPS structure by combining different versions and number of redundant components. The objective of the design is to ensure reliability requirements using the least costs, which could be formulated as a redundancy optimization problem under reliability constraints. The genetic algorithm is one of the most frequently used method for solving redundancy optimization problems. In traditional genetic algorithms, the population size needs to be determined based on the experience of the modeler. Often, this ends up creating a large number of unnecessary samples. As a result, the computational burden can be huge, especially for large-scale MSSPS structures. To solve these problems, this paper proposes an optimal structure designing method named as redundancy ordinal optimization. The universal generating function technique is applied to evaluate the reliabilities of the MSSPSs. Based on the reliabilities, an ordinal optimization algorithm is adapted to update the parent populations and the stopping criterion of genetic algorithm, so that the unnecessary structure designs can be eliminated. Numerical examples show that the proposed method improves the computational efficiency while remaining satisfactorily accurate.

scholarly journals Genetic Algorithm Based Approach for Reliability Redundancy Allocation Problems in Fuzzy Environment

2017 ◽  
Vol 2 (4) ◽  
pp. 259-272 ◽  
Author(s):  
Laxminarayan Sahoo
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Fuzzy Number ◽  
Optimization Problem ◽  
Sensitivity Analyses ◽  
Function Technique ◽  
Constrained Optimization Problem ◽  
Redundancy Allocation ◽  
Fuzzy Environment ◽  
Real Coded Genetic Algorithm

This paper presents the use of genetic algorithm to solve reliability redundancy allocation problem of complicated system in fuzzy environment. Generally, this problem has been formulated as single objective integer non-linear programming problem with several resource constraints. In this paper, the reliability of each component as well as other parameters related to the problem is considered to be fuzzy valued. In this work, the corresponding constrained optimization problem has been transformed to crisp constrained optimization problem using defuzzification of fuzzy number. Here, widely known Yager ranking Index has been used for defuzzification of fuzzy number. The Big-M penalty function technique has been used to transform the constrained optimization problem into an unconstrained optimization problem. The converted problem has been solved with the help of real coded genetic algorithm. To illustrate the proposed methodology, a numerical example has been considered and solved. To study the performance of the proposed genetic algorithm, sensitivity analyses have been done graphically.

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