Optimal concurrent dimensional and geometrical tolerancing based on evolutionary algorithms

A general new methodology using evolutionary algorithm viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for obtaining optimal tolerance allocation and alternative process selection for mechanical assembly is presented. The problem has a multi-criterion character in which 3 objective functions, 6 constraints and 11 variables are considered. The average fitness membership function method is used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MOPSO algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MOPSO are best for this problem.