scholarly journals A hybrid genetic algorithm for ROADEF'05-like complex production problems

DYNA ◽  
2015 ◽  
Vol 82 (190) ◽  
pp. 82-88
Author(s):  
Mariano Frutos ◽  
Ana Carolina Olivera ◽  
Fernando Tohmé
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Methods ◽  
Hybrid Genetic Algorithm ◽  
Hybrid Technique ◽  
Adaptive Search ◽  
Population Improvement ◽  
Annealing Procedure ◽  
Planning Problems ◽  
Production Problems ◽  
Simulated Annealing Procedure

In this work, we present a hybrid technique that combines a Genetic Algorithm with meta-heuristics to solve a problem in RENAULT France’s production plants. The method starts with an initial solution obtained by means of a GRASP (Greedy Randomized Adaptive Search Procedure) used as an input for a Genetic Algorithm complemented by a Simulated Annealing procedure of population improvement. We establish a comparison point among the different techniques used in the method. Their performances are evaluated as well as that of the entire method. The conclusion is that hybrid methods have clear advantages for the treatment of production planning problems.

A New Hybrid Genetic Algorithm and Particle Swarm Optimization

2012 ◽  
Vol 498 ◽  
pp. 115-125 ◽  
Author(s):  
H. Hachimi ◽  
Rachid Ellaia ◽  
A. El Hami
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Hybrid Technique ◽  
Swarm Optimization ◽  
New Generation ◽  
Crossover And Mutation

In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted asGA-PSO, this hybrid technique incorporates concepts fromGAandPSOand creates individuals in a new generation not only by crossover and mutation operations as found inGAbut also by mechanisms ofPSO. The performance of the two algorithms has been evaluated using several experiments.

scholarly journals GUIDED HYBRID GENETIC ALGORITHM FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

2021 ◽  
pp. 174-188
Author(s):  
S. E. Avramenko ◽  
T. A. Zheldak ◽  
L. S. Koriashkina
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Local Search ◽  
Optimization Problems ◽  
Hybrid Methods ◽  
Hybrid Genetic Algorithm ◽  
Solution Process ◽  
Nonlinear Function ◽  
Search Space ◽  
Guided Local Search

Context. One of the leading problems in the world of artificial intelligence is the optimization of complex systems, which is often represented as a nonlinear function that needs to be minimized. Such functions can be multimodal, non-differentiable, and even set as a black box. Building effective methods for solving global optimization problems raises great interest among scientists. Objective. Development of a new hybrid genetic algorithm for solving global optimization problems, which is faster than existing analogues. Methods. One of the crucial challenges for hybrid methods in solving nonlinear global optimization problems is the rational use of local search, as its application is accompanied by quite expensive computational costs. This paper proposes a new GBOHGA hybrid genetic algorithm that reproduces guided local search and combines two successful modifications of genetic algorithms. The first one is BOHGA that establishes a qualitative balance between local and global search. The second one is HGDN that prevents reexploration of the previously explored areas of a search space. In addition, a modified bump-function and an adaptive scheme for determining one of its parameters – the radius of the “deflation” of the objective function in the vicinity of the already found local minimum – were presented to accelerate the algorithm. Results. GBOHGA performance compared to other known stochastic search heuristics on a set of 33 test functions in 5 and 25dimensional spaces. The results of computational experiments indicate the competitiveness of GBOHGA, especially in problems with multimodal functions and a large number of variables. Conclusions. The new GBOHGA hybrid algorithm, developed on the basis of the integration of guided local search ideas and BOHGA and HGDN algorithms, allows to save significant computing resources and speed up the solution process of the global optimization problem. It should be used to solve global optimization problems that arise in engineering design, solving organizational and management problems, especially when the mathematical model of the problem is complex and multidimensional.

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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