Intelligent optimization techniques, genetic algorithms, tabu search, simulated annealing, and neural networks, D. T. Pham and D. Karaboga, Springer: Berlin, Heidelberg, New York; Springer London: London, 2000, 302pp, ISBN 1-85233-028-7

2005 ◽  
Vol 15 (6) ◽  
pp. 287-289 ◽  
Author(s):  
Marco P. Schoen
Keyword(s):  
Neural Networks ◽  
New York ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Optimization Techniques ◽  
Intelligent Optimization

scholarly journals Sign Language Recognition using Hybrid Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 1092-1098
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Sign Language ◽  
Back Propagation ◽  
Optimization Techniques ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Sign Languages ◽  
Artificial Neural

Language has a prime role in communication between persons, in learning, in distribution of concepts and in preserving public contacts. The hearing-impaired have to challenge communication obstacles in a mostly hearing-capable culture. There are hundreds Sign Languages that are used all around the world today .The Sign Languages are established depending on the country and area of the deaf public. The aim of sign language recognition is to offer an effectual and correct tool to transcribe hand gesture into text. It can play a vital role in the communiqué between deaf and hearing society. Sign language recognition (SLR), as one of the significant research fields of human–computer interaction (HCI), has produced more and more interest in HCI society. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for sign language recognition in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system.

scholarly journals Analysis of Transmission Cost Allocation Strategies with Reliability for Deregulated Systems

2019 ◽  
Vol 8 (9S3) ◽  
pp. 391-397
Keyword(s):  
Genetic Algorithms ◽  
Tabu Search ◽  
Cost Allocation ◽  
Optimization Techniques ◽  
Transmission Cost ◽  
The Cost ◽  
Allocation Strategies

This paper presents a learning review of various strategies related to the improvement of the reliability for the deregulated system, for instance, Genetic Algorithms (GA), Tabu Search (TS), heuristic calculations and system based techniques. These methodologies were produced for advancing reliability as either software or hardware exclusively. Besides, the cost segments related with limit utilize and reliability advantage charges are resolved and various optimization techniques are acknowledged of action of the goal work

scholarly journals Optimizing Day-Ahead Electricity Market Prices: Increasing the Total Surplus for Energy Exchange Istanbul

2020 ◽  
Vol 22 (4) ◽  
pp. 700-716 ◽  
Author(s):  
Kursad Derinkuyu ◽  
Fehmi Tanrisever ◽  
Nermin Kurt ◽  
Gokhan Ceyhan
Keyword(s):  
Genetic Algorithms ◽  
Tabu Search ◽  
Social Welfare ◽  
Electricity Market ◽  
Energy Exchange ◽  
Heuristic Algorithms ◽  
Optimization Techniques ◽  
Problem Definition ◽  
Combinatorial Auction ◽  
Mixed Integer

Problem definition: We design a combinatorial auction to clear the Turkish day-ahead electricity market, and we develop effective tabu search and genetic algorithms to solve the problem of matching bidders and maximizing social welfare within a reasonable amount of time for practical purposes. Academic/practical relevance: A double-sided blind combinatorial auction is used to determine electricity prices for day-ahead markets in Europe. Considering the integer requirements associated with market participants’ bids and the nonlinear social welfare objective, a complicated problem arises. In Turkey, the total number of bids reaches 15,000, and this large problem needs to be solved within minutes every day. Given the practical time limit, solving this problem with standard optimization packages is not guaranteed, and therefore, heuristic algorithms are needed to quickly obtain a high-quality solution. Methodology: We use nonlinear mixed-integer programming and tabu search and genetic algorithms. We analyze the performance of our algorithms by comparing them with solutions commercially available to the market operator. Results: We provide structural results to reduce the problem size and then develop customized heuristics by exploiting the problem structure in the day-ahead market. Our algorithms are guaranteed to generate a feasible solution, and Energy Exchange Istanbul has been using them since June 2016, increasing its surplus by 448,418 Turkish liras (US$128,119) per day and 163,672,570 Turkish liras (US$46,763,591) per year, on average. We also establish that genetic algorithms work better than tabu search for the Turkish day-ahead market. Managerial implications: We deliver a practical tool using innovative optimization techniques to clear the Turkish day-ahead electricity market. We also modify our model to handle similar European day-ahead markets and show that performances of our heuristics are robust under different auction designs.

Sign in / Sign up

Export Citation Format

Share Document