Two-hybrid sine cosine algorithm based on Invasive weed optimization algorithm and Bat algorithm

This research paper takes two hybrids. First hybrid is a sine cosine algorithm with evolution algorithm invasive weed optimization (IWO) resulting that improved algorithm has highly properties that we so called SCA-IWO. Second hybrid is a sine cosine algorithm (SCA) with Bat algorithm optimization (BA). Which is considered one of the intelligent swarm algorithms related to intelligent arithmetic branches and have got on hybrid algorithm, so called SCA-BA. In these two improved algorithms that we have gained which have high properties that they can overcome over the problems that confront the SCA, IWO and BA, each separately. In addition to that, it can pass the falling in local solutions and slow converge, so they gained the global value in vast majority of numerical calculation. SCA-IWO and SCA-BA haven been applied on set of function by specialists in testing optimization that has high measurement with certain iterations. Numerical results proved the efficiencies of hybrids of SCA-IWO and SCA-BA with excellent results compared with original SCA, as well as getting a global and minimum value in many used functions.