Combinatorial optimization problems (COPs) are challenging class of problems in the field of optimization. Permutations are preferred as solution representation scheme in most cases. Metaheuristic techniques can be used to look for good solutions for COPs with low cost. Moth-flame algorithm (MFO) is one recent population-based metaheuristic technique for continuous optimization problems. In this work improvement of MFO when used to solve COPs is studied. An improved version of MFO (called LCMFO) where Lévy-flight function is used to prepare initial solutions is proposed. Also crossover functions of genetic algorithms are used together with the basic technique of MFO to generate new solutions. Both MFO and LCMFO are tested with travelling salesman problem (TSP) as one popular COP. Experimental results show that there is a notable improvement of about 20-40% in the quality of solutions found by LCMFO over MFO only.