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 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.

Using a new GA-based multiobjective optimization technique for the design of robot arms

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 401-414 ◽  
Author(s):  
Carlos A. Coello Coello ◽  
Alan D. Christiansen ◽  
Arturo Hernández Aguirre
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Ad Hoc ◽  
Optimization Technique ◽  
Hybrid Approach ◽  
Optimization Method ◽  
Robot Arm ◽  
Genetic Operators ◽  
Point Representation ◽  
A New Technique

This paper presents a hybrid approach to optimize the counterweight balancing of a robot arm. A new technique that combines an artificial intelligence technique called the genetic algorithm (GA) and the weighted min-max multiobjective optimization method is proposed. These techniques are included in a system developed by the authors, called MOSES, which is intended to be used as a tool for engineering design optimization. The results presented here show how the new proposed technique can get better trade-off solutions and a more accurate Pareto front for this highly non-convex problem using an ad-hoc floating point representation and traditional genetic operators. Finally, a methodology to compute the ideal vector using a genetic algorithm is presented. It is shown how with a very simple dynamic approach to adjust the parameters of the GA, it is possible to obtain better results than those previously reported in the literature for this problem.

MULTI-OBJECTIVE OPTIMIZATION IN RELIABILITY SYSTEM USING GENETIC ALGORITHM AND NEURAL NETWORK

2008 ◽  
Vol 25 (05) ◽  
pp. 649-672 ◽  
Author(s):  
LIANG-HSUAN CHEN ◽  
CHENG-HSIUNG CHIANG
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
System Optimization ◽  
Evolutionary Strategy ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Reliability Systems

To optimize the design of reliability systems, an analyst is frequently faced with the demand of achieving several targets (i.e., maximization of system reliability, minimizations of cost, volume, and weight), some of which may be in conflict with each other. This paper presents a novel hybrid approach, combining a multi-objective genetic algorithm and a neural network, for multi-objective optimization of a reliability system, namely GANNRS (Genetic Algorithm and Neural Network for Reliability System optimization). The multi-objective genetic algorithm's evolutionary strategy is based on the modified neighborhood design, and is presented to find the Pareto optimal solutions so as to provide a variety of compromise solutions to the decision makers. The purpose of the neural network is to generate a good initial population in order to speed up the searching by genetic algorithm. For demonstrating the feasibility of the proposed approach, four multi-objective optimization problems of reliability system are used, and the outcomes are compared with those from other methods. The evidence shows that the proposed GANNRS is more efficient in computation, and the results from the objectives are appealing.

Large permeability metamaterial design optimization for low frequency applications

2020 ◽  
Vol 64 (1-4) ◽  
pp. 369-375
Author(s):  
Yiying Li ◽  
Shiyou Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Low Frequency ◽  
Primary Objective ◽  
Test Function ◽  
Multi Objective ◽  
Optimal Direction ◽  
Cellular Genetic Algorithm ◽  
Secondary Objective ◽  
Novel Method

This paper presents a novel method for solving multi-objective optimization problems based on single-objective cellular genetic algorithm. In the proposed multi-objective cellular genetic algorithm, the objectives are divided into the primary objective and the secondary objective according to the preferences of a decision maker. The primary objective is used as the driving force for individual updating, while the secondary objective is employed as the bias force to select neighbors. The proposed approach has ensured that the secondary objective is also evolving in the optimal direction, as evidenced by the numerical results on both a mathematical test function and a prototype metamaterial unit as reported in this paper.

Stellar Evolution

1962 ◽  
Vol 11 (02) ◽  
pp. 137-143
Author(s):  
M. Schwarzschild
Keyword(s):  
Solar System ◽  
Stellar Evolution ◽  
Space Craft ◽  
New Techniques ◽  
Substantial Body ◽  
Individual Stars ◽  
The Past ◽  
Evolution Of Galaxies ◽  
History Of ◽  
Fast Flow

It is perhaps one of the most important characteristics of the past decade in astronomy that the evolution of some major classes of astronomical objects has become accessible to detailed research. The theory of the evolution of individual stars has developed into a substantial body of quantitative investigations. The evolution of galaxies, particularly of our own, has clearly become a subject for serious research. Even the history of the solar system, this close-by intriguing puzzle, may soon make the transition from being a subject of speculation to being a subject of detailed study in view of the fast flow of new data obtained with new techniques, including space-craft.

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

scholarly journals Escaping the past and living in the present: a qualitative exploration of substance use among Syrian male refugees in Germany

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Jutta Lindert ◽  
Ulrike Neuendorf ◽  
Marta Natan ◽  
Ingo Schäfer
Keyword(s):  
Substance Use ◽  
Hybrid Approach ◽  
Syrian Refugees ◽  
Group Discussions ◽  
The Past ◽  
Audio Recordings ◽  
Psychosocial Difficulties ◽  
Professional Treatment ◽  
And Coping ◽  
Qualitative Exploration

Abstract Background Syrians have been the largest group of refugees in Germany since 2014. Little is known about Syrian refugees` perspectives on substance use. The aim of this study is to investigate the perspective of male refugees from Syria and to foster specific knowledge and understanding of substance use. Methods We applied a qualitative study design. Five semi-structured focus group discussions with a total of 19 refugees were conducted in 2019 among the difficult to reach population of Syrian refugees. Audio recordings were translated and transcribed. We used a hybrid approach by integrating inductive and deductive thematic frameworks. Results We identified common themes. Firstly, refugees perceived that substances are widely available and accepted in Germany. Secondly, refugees perceived that rules and norms in Germany differ from rules and norms in the home country and favor availability of substances. Thirdly, substance use is related to the intention to escape the past. Fourthly, substance use is related to living in the present through connecting with others and being part of the community. Finally, mental health professional treatment for substance use is associated with shame. Conclusions Findings support Syrian refugees` perspectives of substance use as a way of both escaping the past and coping with psychosocial difficulties in the present in a socio-ecological understanding. Understanding the explanatory model of Syrian refugees can inform future interventions to prevent substance abuse and design tailored interventions. Further studies with Syrian refugees in more countries are needed to better understand resettled refugees` perspectives on substance use.

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