Constraint handling improvements for multiobjective genetic algorithms

Abstract

Four constraint handling improvements for Multi-Objective Genetic Algorithms (MOGA) are proposed. These improvements are made in the fitness assignment stage of a MOGA and are all based upon a “Constraint-First-Objective-Next" model. Two multi-objective design optimization examples, i.e. a speed reducer design and the design of a fleet of ships, are used to demonstrate the improvements. For both examples, it is shown that the proposed constraint handling techniques significantly improve the performance of a baseline MOGA.