Multi-objective quasi oppositional Jaya algorithm to solve multi-objective solid travelling salesman problem with different aspiration level

Multi-Objective Travelling Salesman Problem (MOTSP) is one of the most crucial problems in realistic scenarios and is difficult to solve by classical methods. However, it can be solved by evolutionary methods. This paper presents an Aspiration Level based Multi-Objective Quasi Oppositional Jaya (AL-based MOQO-Jaya) Algorithm for solving the Multi-Objective Solid Travelling Salesman Problem (MOSTSP). Furthermore, fuzzy judgment was characterised by utilizing the possibility and necessity measures to allow the decision-maker (DM) for optimising different scenarios of the Fuzzy Multi-Objective Solid Travelling Salesman Problem (FMOSTSP). A numerical illustration is provided for 10, 80, 100 and 120 cities, and sensitivity analysis is performed with different shape parameters and aspiration levels. Further the results are compared by CPLEX optimizer and Hybrid GA. The results obtained by AL-based MOQO Jaya are more efficient, the run time of AL-based MOQO is 5.8667, 40.9115, 58.4789 and 60.6882 seconds for 10, 80,100 and 120 cities for FMOSTSP which is quite less as compared to CPLEX and Hybrid GA. To access the performance of the proposed method coverage and hypervolume are calculated. This paper concludes that developed approach has solved MOSTSP and FMOSTSP efficiently with an effective output and provides alternative solutions for decision-making to DM.