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Solving Multi-Objective Optimization Problems using Differential Evolution Algorithm with Different Population Initialization Techniques

Publication Type : Conference Paper

Publisher : 2018 International Conference on Advances in Computing, Communications and Informatics

Source : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2018)

Url : https://ieeexplore.ieee.org/document/8554861

Keywords : algorithmic complexity, algorithmic strategies, Benchmark testing, Complexity theory, Differential Evolution, differential evolution algorithm, Evolutionary Algorithm, Evolutionary algorithms, evolutionary computation, evolutionary computing community, IP networks, Linear programming, Multi-objective optimization problem, multiobjective optimization problems, mutliobjective optimization problems, Optimization, optimization problem, PI techniques, Population Initialization, population initialization techniques, sociology, Statistics

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2018

Abstract : The researchers of Evolutionary Computing (EC) community proposing new and different algorithmic strategies to tackle the increasing issues in handling optimization problems. As the number of objectives in an optimization problem increases the algorithmic complexity in solving the problem also increases. The way the initial population for an optimization problem generated is greatly affecting the performance of the Evolutionary Algorithms (EAs). This paper investigates the performance of Differential Evolution (DE) in solving Mutli-Objective optimization problems (MOOP) with two different population initialization (PI) techniques. The performance of different instances of DE is compared based on the solution accuracy obtained. The results obtained shows that DE shows different performance for different PI techniques.

Cite this Research Publication : K. Devika and Dr. Jeyakumar G., “Solving Multi-Objective Optimization Problems using Differential Evolution Algorithm with Different Population Initialization Techniques”, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018.

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