Is differential evolution with penalty function really better than CMODE?

Differential Evolution is used in global optimization over continuous spaces. In general, the task is to optimize certain properties of a system by pertinently choosing the system parameters. Differential evolutions have been widely used to solve difficult constrained optimization problem. Differential evolution gives good results with unconstrained problem. In this paper Differential evolution is used for constrained problem with constraints handling techniques. Penalty function is a most widely used constraints handling technique which is used in this paper. Basically there are two kind of penalty function one is static penalty and other one is dynamic. Cai and Wang´s method is a recent constrained optimization evolutionary algorithm. However, its main shortcoming is that a trial- and-error process has to be used to choose suitable parameters. To overcome the above shortcoming, an improved version of the CW [31] method, called CMODE [30], which combines multiobjective optimization with differential evolution to deal with constrained optimization problems. individuals in CMODE is also based on multiobjective optimization. In CMODE, however, differential evolution serves as the search engine. In addition, a novel infeasible solution replacement mechanism based on multiobjective optimization is used, with the purpose of guiding the population toward promising solutions and the feasible region simultaneously. Differential evolution with static penalty function is used here which gives great quality of solution. And in this paper we compared the solution of Differential Evolution with Static penalty function and CMODE [30].