Performance of Infeasibility Empowered Memetic Algorithm (IEMA) on Engineering Design Problems

Engineering design optimization problems often involve a number of constraints. These constraints may result from factors such as practicality, safety and functionality of the design and/or limit on time and resources. In addition, for many design problems, each function evaluation may be a result of an expensive computational procedure (such as CFD, FEA etc.), which imposes a limitation on the number of function evaluations that can be carried out to find a near optimal solution. Consequently, there is a significant interest in the optimization community to develop efficient algorithms to deal with constraint optimization problems. In this paper, a new memetic algorithm is presented, which incorporates two mechanisms to expedite the convergence towards the optimum. First is the use of marginally infeasible solutions to intensify the search near constraint boundary, where optimum solution(s) are most likely to be found. Second is performing local search from promising solutions in order to inject good quality solutions in the population early during the search. The performance of the presented algorithm is demonstrated on a set of engineering design problems, using a low computation budget (1000 function evaluations).

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