Designing a Meta-Huristic Algorithm based on Two Mathematical Functions: Tanh(x) and xcos(x)

Nowadays using meta-heuristic algorithms is increasing in finding sub-optimal solutions for combinatorial problems in reasonable time. Model Developers tend to approach model toward real world. Consequently the complexity of models increase and conventional mathematical solving methods fail in finding optimal point(s) by using reasonable resources. In this circumstance using meta-heuristics is unavoidable. Developing meta-heuristics usually inspire from nature. Examples of these algorithms are: Genetic Algorithm, Ant Colony Optimization, Simulated annealing, and so on.This paper proposed an algorithm based on behavior of xCos(x) and tanh(x) in loops. The algorithm can be used in continuous object functions. Experimental tests and statistical analysis show the quality and speed of proposed algorithm is better than Simulated Annealing (SA) and Cloud theory base simulated annealing (CSA). Excellency of the algorithm in finding global optimum of seven benchmark function proved.