Applications of Multi-Objective Evolutionary Algorithms in Engineering Design


Engineering design is a multidisciplinary and multifaceted activity that requires simultaneous consideration of various design requirements and resource constraints. Such problems are inherently multiobjective in nature and involve highly nonlinear objectives and constraints often with functional and slope discontinuity that limits the effective use of gradient based optimization methods for their solution. Furthermore, in absence of preference information among the objectives, the goal in a multi-objective optimization problem is to arrive at a set of Pareto optimal designs. Evolutionary algorithms are particularly attractive in solving such problems as they are essentially stochastic, zero-order methods which maintain a set of solutions as a population and improves them over generations. In order for an optimization algorithm to be an effective design tool, it should be computationally efficient and easy to use with minimal number of user inputs. A number of engineering design optimization examples are presented here and solved using a multi-objective evolutionary algorithm. The examples clearly demonstrate the benefits offered by multi-objective optimization and highlight the key features of the evolutionary algorithm.