A hybrid genetic algorithm based on quadratic penalty function for BLT light source reconstruction

2010 ◽  
Author(s):  
Shuping Gao ◽  
Qitan Zhang ◽  
Ling Zhang
Keyword(s):  
Genetic Algorithm ◽  
Light Source ◽  
Penalty Function ◽  
Hybrid Genetic Algorithm ◽  
Source Reconstruction ◽  
Quadratic Penalty Function

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

2012 ◽  
pp. 643-662
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Objective Function ◽  
Penalty Function ◽  
Manufacturing Systems ◽  
Optimization Technique ◽  
Hybrid Genetic Algorithm ◽  
Decision Variable ◽  
Exponential Penalty Function ◽  
Simulation Scenario

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

2011 ◽  
pp. 212-231 ◽  
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Objective Function ◽  
Penalty Function ◽  
Manufacturing Systems ◽  
Optimization Technique ◽  
Hybrid Genetic Algorithm ◽  
Decision Variable ◽  
Exponential Penalty Function ◽  
Simulation Scenario

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

scholarly journals A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box

2016 ◽  
Vol 10 (2) ◽  
pp. 67 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Efficient Global Optimization ◽  
Global Optimization Method

<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.</p>Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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