Abstract
The optimization of non-linear constrained problems is relevant to chemical engineering practice (Salcedo, 1992; Floudas, 1995). In recent years, evolutionary algorithms (EAs) have been applied to the solution of NLP in many engineering applications. The best-known algorithms in this class include Genetic Algorithms (GA), Evolutionary Programming (EP), Evolution Strategies (ES) and Genetic Programming (GP). There are many hybrid systems, which incorporate various features of the above paradigms and consequently are hard to classify, which can be referred just as EC methods (Dasgupta and Michalewicz, 1997). They differ from the conventional algorithms since, in general, only the information regarding the objective function is required. EC methods have been applied to a broad range of activities in process system engineering including modeling, optimization and control. Differential Evolution (DE), developed by Price & Storn (1997), is one of the best EC methods. This method provides one of the best genetic algorithms for solving the real-valued test function. The convergence speed of DE is very high.
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