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DNA Double Helix Based Hybrid GA for the Gasoline Blending Recipe Optimization Problem
Author(s) -
Tao J.,
Wang N.
Publication year - 2008
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.200700322
Subject(s) - sequential quadratic programming , recipe , mathematical optimization , nonlinear programming , convergence (economics) , computer science , global optimization , reliability (semiconductor) , algorithm , code (set theory) , mathematics , nonlinear system , quadratic programming , chemistry , power (physics) , physics , food science , set (abstract data type) , quantum mechanics , programming language , economics , economic growth
A hybrid genetic algorithm is proposed for heavily nonlinear constrained optimization problems by utilizing the global exploration and local exploitation characteristics, and the convergence rate of the proposed algorithm is analyzed. In the global exploration phase, a DNA double helix structure is used to overcome Hamming cliffs and DNA computing based operators are applied to improve the global searching capability. When the feasible domains are located, the sequential quadratic programming (SQP) method is performed to quickly find the local optimum and improve the solution accuracy. The comparison results of typical numerical examples and the gasoline blend recipe optimization problem are employed to demonstrate the reliability and efficiency of the proposed algorithm.