An imprecise Multi Objective Genetic Algorithm for uncertain Constrained Multi Objective Solid Travelling Salesman Problem
New imprecise Multi Objective Genetic Algorithm (iMOGA) uses fuzzy age selection.Also adaptive crossover and generation dependent mutation are used in iMOGA.Fuzzy extended age based selection is also used in place of fuzzy age based selection.Constrained Solid TSPs are solved with random, fuzzy random, random fuzzy and bi random data.iMOGA is tested with TSPLIB data and its supremacy also proved by ANOVA test. In this paper, an imprecise Multi Objective Genetic Algorithm (iMOGA) is developed to solve Constrained Multi Objective Solid Travelling Salesman Problems (CMOSTSPs) in crisp, random, random fuzzy, fuzzy random and bi random environments. In the proposed iMOGA, '3 and 5 level linguistic based age oriented selection' 'probabilistic selection' and an 'adaptive crossover' are used along with a new generation dependent mutation. In each environment, some sensitivity studies due to different risk/discomfort factors and other system parameters are presented. To test the efficiency, combining same size single objective problems from standard TSPLIB, the results of such multi objective problems are obtained by the proposed algorithm, simple MOGA (Roulette wheel selection, cyclic crossover and random mutation) NSGA II, MOEA D/ACO and compared. Moreover, a statistical analysis (Analysis of Variance) is carried out to show the supremacy of the proposed algorithm.
Expert Syst. Appl.