Improved mean variance mapping optimization for the travelling salesman problem

This paper presents an improved Mean Variance Mapping Optimization to address and solve the NP-hard combinatorial problem, the travelling salesman problem. MVMO, conceived and developed by István Erlich is a recent addition to the large set of heuristic optimization algorithms with a strategic novel feature of mapping function used for mutation on basis of the mean and variance of the population set initialized. Also, a new crossover scheme has been proposed which is a collective of two crossover techniques to produce fitter offsprings. The mutation technique adopted is only used if it converges towards more economic traversal. Also, the change in control parameters of the algorithm doesn’t affect the result thus making it a fine algorithm for combinatorial as well as continuous problems as is evident from the experimental results and the comparisons with other algorithms which has been tested against the set of benchmarks from the TSPLIB library.