Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)

A hybrid micro genetic algorithm (HMGA) is proposed for Pareto optimum search focusing on the Kursawe and ZDT test functions. HMGA is a fusion of the micro genetic algorithm (MGA) and the elitism concept of fast Pareto genetic algorithm. The effectiveness of HMGA in Pareto optimal convergence was in...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2015-12, Vol.19 (12), p.3571-3580
Main Authors: Lim, Wei Jer, Jambek, Asral Bahari, Neoh, Siew Chin
Format: Article
Language:English
Subjects:
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Artificial Intelligence
Computational Intelligence
Control
Elitism
Engineering
Focus
Genetic algorithms
Linear programming
Mathematical Logic and Foundations
Mechatronics
Objectives
Optimization
Pareto optimum
Robotics
Variables
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Summary:A hybrid micro genetic algorithm (HMGA) is proposed for Pareto optimum search focusing on the Kursawe and ZDT test functions. HMGA is a fusion of the micro genetic algorithm (MGA) and the elitism concept of fast Pareto genetic algorithm. The effectiveness of HMGA in Pareto optimal convergence was investigated with two performance indicators (i.e. generational distance and spacing). To measure HMGA’s performance, a comparison study was conducted between HMGA and MGA. In this work, HMGA is outperformed MGA in the search for Pareto optimal front and capable of solving different difficulty of MOPs.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1767-5