PERFORMANCE OF HYBRID REAL CODED GENETIC ALGORITHMS

Abstract

International Journal of Computational Engineering ScienceVol. 02, No. 04, pp. 583-601 (2001) No AccessPERFORMANCE OF HYBRID REAL CODED GENETIC ALGORITHMSS. BASKAR, P. SUBBARAJ and M. V. C. RAOS. BASKARElectrical Engineering Department, Thiagarajar College of Engineering, Madurai – 625 015, Tamilnadu, India Search for more papers by this author , P. SUBBARAJElectrical Engineering Department, Thiagarajar College of Engineering, Madurai – 625 015, Tamilnadu, India Search for more papers by this author and M. V. C. RAOElectrical Engineering Department, Indian Institute of Technology (IIT), Chennai-600 036, Tamilnadu, India Search for more papers by this author https://doi.org/10.1142/S1465876301000465Cited by:22 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThis paper presents a new robust, two -phase hybrid optimization algorithm to solve non-linear constrained optimization problems. The proposed algorithm is very much useful when addressing heavily constrained optimization problem in terms of solution accuracy and computation time. It can out perform conventional genetic algorithms (CGAs) is the sense that Hybrid GAs makes it possible to improve both the quality of the solution and reduce the computing expenses. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local search method is next employed to do fine tuning. The phase - 1 uses real coded genetic algorithm, while optimization by direct search and systematic reduction of the size of search region method is employed in the phase - 2. Thirteen test problems were taken from the literature have been simulated and results obtained were compared. The results clearly demonstrate that the proposed hybrid real coded genetic algorithm not only improve the reliability but also make the algorithm more efficient in terms of number of function evaluations to reach the global optimum point. Thus the usefulness and effectiveness of this algorithm is placed in a sharper focus. 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