Title:Performance Analysis of Optimization Process on Adaptive Group of Ink Drop Spread Algorithm Proficiency
VOLUME: 13 ISSUE: 6
Author(s):Iman E.P. Afrakoti and Vahdat Nazerian*
Affiliation:Department of Electrical & Computer Engineering, University of Mazandaran, Babolsar, Department of Electrical & Computer Engineering, University of Mazandaran, Babolsar
Keywords:AGIDS, evolutionary algorithm, genetic algorithm, fuzzy inference, particle swarm optimization, linguistic fuzzy.
Abstract:
Aims: Two evolutionary algorithms consist of Genetic Algorithm (GA) and Particle
Swarm Optimization (PSO) are being used for finding the best value of critical parameters in
AGIDS which will affect the accuracy and efficiency of the algorithm.
Background: Adaptive Group of Ink Drop Spread (AGIDS) is a powerful algorithm which was
proposed in fuzzy domain based on Active Learning Method (ALM) algorithm.
Objective: The effectiveness of AGIDS vs. artificial neural network and other soft-computing algorithms
has been shown in classification, system modeling and regression problems.
Methods: For solving a real-world problem a tradeoff should be taken between the needed accuracy
and the available time and processing resources.
Results: The simulation result shows that optimization approach will affect the accuracy of modelling
being better, but its computation time is rather high.
Conclusion: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex
problems without using complex optimization algorithms.
Other: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex
problems without using complex optimization algorithms.
