Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms II: Application Example

The evolutionary approach to multiple function optimization formulated in the first part of the paper (1) is applied to the optimization of the low-pressure spool speed governor of a Pegasus turbine engine. This study illustrates how a technique such as the mUltiobjective Genetic Algorithm can be applied and exemplifies how design requirements can be defined as the algorithm runs. Several objective functions and associated goals express design concerns in direct form, i.e., as the designer would state them. While such a designer-orientated formulation is very attractive, its practical usefulness depends heavily on the ability to search and optimize cost surfaces in such a class much broader than usual, as already provided to a large extent by the Genetic Algorithm (GA). The two instances of the problem studied, demonstrate the need for preference articulation in cases where many and highly competing objects lead to a non dominated set too large for a finite population to sample effectively. Further, it is sown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to supply preference information to the GA. The paper concludes with a discussion of the results.