Abstract
The Evolutionary Computation has grown much in last few years. Inspired by biological evolution, this field is used to solve NP-hard optimization problems to come up with best solution. TSP is most popular and complex problem used to evaluate different algorithms. In this paper, we have conducted a comparative analysis between NSGA-II, NSGA-III, SPEA-2, MOEA/D and VEGA to find out which algorithm best suited for MOTSP problems. The results reveal that the MOEA/D performed better than other three algorithms in terms of more hypervolume, lower value of generational distance (GD), inverse generational distance (IGD) and adaptive epsilon. On the other hand, MOEA-D took more time than rest of the algorithms.
Highlights
The optimization problems with a single objective are relatively easy to solve but in case of more than one objectives the optimization become harder and these kinds of problems are very common in the existing world
Heuristic Methods are based on approximations of Pareto Solutions (PS) and Pareto Front (PF) of multi objective traveling salesman problem (MOTSP)
A new extension named Multi Objective Evolutionary Algorithm derived from Decomposition with Ant Colony Optimization (MOEA/DACO) [9] which was proposed based on the idea that each ant will be responsible for one sub problem
Summary
The optimization problems with a single objective are relatively easy to solve but in case of more than one objectives the optimization become harder and these kinds of problems are very common in the existing world. Heuristic Methods are based on approximations of Pareto Solutions (PS) and Pareto Front (PF) of multi objective traveling salesman problem (MOTSP). To deal with more than three objectives problems, (ManyObjective) the NSGA-II did not prove to be very effective a new solution was proposed called NSGA-III [7] which was an extension of NSGA-II algorithm. To solve different complex Multi Objective Problems (MOPs), different extensions of Multi Objective Evolutionary Algorithm based on Decomposition (MOEA/D) have been practiced. A new extension named Multi Objective Evolutionary Algorithm derived from Decomposition with Ant Colony Optimization (MOEA/DACO) [9] which was proposed based on the idea that each ant will be responsible for one sub problem. The MOEA/D-ACO was compared with BicriterionAnt [10] algorithm by applying it on dual objectives traveling Salesman Problem (b-TSP) and improvement has been clearly observed.
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