An easy-to-implement differential evolution approach for multi-objective optimizations
Purpose - To present and validate a new differential evolution (DE) method for multi-objective optimization method. Design/methodology/approach - A new selection scheme was designed to replace the existing one in DE to enable DE applicable to either single objective or multi-objective optimizations. Findings - The new method was validated with three simple multi-objective optimization problems. The simulation results show that the approach is capable of generating an approximated Pareto-front for each selected problem. The new DE method was used to optimize a prototype air mixer subject to two objective functions to be minimized. The results demonstrate that the new DE approach can handle this practical multi-objective problem successfully. Originality/value - The new method is an easy-to-implement evolutionary method and has the potential for application for any complicated engineering optimizations.
prototypes optimization techniques pareto analysis algorithmic languages