Adapting electromagnetism-like heuristics for solving travelling salesman problems

Nowadays, in order to solve such combinatorial and complicated problems like vehicle routing and sales traveling problems which are seen especially logistics fields, different heuristic algorithms and artificial intelligence methods are proposed as an alternative for mathematical programming methods. Especially, the multi combinational structure of vehicle routing problems is one of the most important factors in the choice of solving methods. Thus, owing to stochastic and iterative structure, meta-heuristic algorithms have a big practicality to find or converge the optimal solution in the situations when it is difficult to create mathematical model. Heuristics algorithms can be used solely to find the optimal value of objective function; however they can be applied by hybridizing them with different searching algorithms and different heuristics in order to develop their ability of finding optimal solution in different problem types. We handled Electromagnetism-like heuristic algorithms which is started to use in recent years. Actually, this algorithm has been introduced for global optimization problems. First, we applied this applied onto 3 dimensional functions and observed that this algorithm is successfully able to find the global optimum point in suitable computational times. After getting good results from continuous functions, this algorithm has been hybridized by integrating the Random Key mechanism in order to get it convenient to solve actual multi-parametric, multivariate, discrete and constrained optimization problems such as scheduling, sequencing, assignment, vehicle routing problems. The computational results have been showed that, the proposed Electromagnetism-like Algorithm gives good results on travelling salesman problems within suitable computational times with respect to different meta-heuristic algorithms.