432 Research Article International Journal of Current Engineering and Technology ISSN 2277 - 4106 © 2012 INPRESSCO. All Rights Reserved. Available at http://inpressco.com/category/ijcet Performance Improvement of Differential Evolutionary Algorithm: A Survey Vanita G. Tonge a* and P.S.Kulkarni a a Department of Information Technology, RCERT, Chandrpur Accepted 4 Nov.2012, Available online 27Dec. 2012, Vol.2, No.4(Dec. 2012) Abstract Differential evolution is one of the latest evolutionary optimization algorithm applied to continuous optimization problems. Differential evolution for solving flow shop problem that belongs to the class of scheduling problems. The scheduling problems arise in diverse areas such as manufacturing systems, production planning, computer design, logistics etc.. Only in very special cases there exist exact polynomial algorithms to reach optimal solution. To solve PFSP problem many researchers have used different EVs algorithm such as ACO, PSO, GA. Out of such evolutionary algorithm DE is the best solution. Keywords: Differential Evolution, recombination, PSO, ACO, perturbation, PFSP. 1. Introduction 1 Evolutionary Computation (EC) uses ideas inspired from the behavior of community-based animals such as ants, bees and birds. Furthermore researchers proposed algorithms with variants of DE. Differential Evolution (DE) algorithm has emerged as a very competitive form of evolutionary computing more than a decade ago. First a donor vector (V i,t ) is created in step a and then a trial vector (U i,t ) is created in step b by stochastically combining elements from X i,t and V i,t . This combination is commonly done using an exponential distribution with crossover factor of CR. If the newly created child U i,t is better compared to X i,t then U i,t is stored for updating X i,t+1 . It should be noted carefully that X i,ts are updated to X i,t+1s after entire set of U i,ts are created. Once the population is updated, the generation counter is incremented and a termination criterion is checked. Following properties of this DE should be noted: (i) There is „elitism‟ at an individual level i.e. if the newly created trial vector Ui,t is inferior compared to the individual then individual is preserved as a child for the next generation and Vi,t is ignored. (ii) The algorithm follows a generational model i.e. the current population is updated only after the entire offspring population is created. One of the noticeable features of standard DE is elitism at the individual level i.e. a child is compared with its base parent (i.e. the individual at the index corresponding to which child has been created), and only the better of the two survives for the next generation. We modified this Replacement scheme by always accepting the newly created child i.e. without carrying out the parent-child comparison. This resulted in significant * Vanita G. Tonge is M.Tech Scholar and Prof.P.S.Kulkarni is the Project Guide. performance degradation in all three test problems with respect to both the metrics, indicating that elitism in DE by parent-child comparison is key to its performance. (Nikhil et al,2010) DE has several advantages: it can search randomly, requires only fewer parameters setting, high performance and applicable to high-dimensional complex optimization problems. But similar to PSO, DE has several drawbacks including unstable convergence in the last period and easy to drop into regional optimum. Compared with else evolutionary computation, DE and PSO have advantage respectively (Ying-Chih et al,2009). Performance of DE is based on its control parameters. Researchers have provided the various techniques to improve the performance of DE. We will see it in next section. The remaining paper is organized as follows. Section 2 introduces the differential evolution (DE) algorithm. Section 3 present PFSP problems, Section 4 gives brief review on performance improvement of DE, proposed plan is discussed in Section 5.Finally, Section 6 summarizes the concluding remark. 2. Differential Evolution (DE) Differential Evolution is announced in 1995 by Price and Storn, and its superior performance in solving complex problems.( D. G. Mayer et al,2005).DE is based on individual‟s difference, utilize random research in solution space, further utilize the mechanism “mutation”, “recombination”, “selection” to compute every individuals to obtain appropriate individual. DE used the information between the difference in individuals to lead to search, thus the result of search is more unstable(Z.-F. Hao et, 2007).The advantage and weakness of DE is given as follows.