Mother Tree Optimization Algorithm (MTO)

MTO is a new algorithm that I have proposed and it is very powerful to solve continuous and discrete optimization problems

Mother Tree Optimization (MTO) for solving continuous optimization problems. MTO uses a set of cooperating agents that evolve based on the communication between Douglas fir trees mediated by the mycorrhizal fungi network that transfers nutrients between plants of the same or different species. In order to assess the performance of the MTO algorithm, we conducted extensive experiments on its variants, with and without climate change. In this regard, we run several statistical and graphical analyses on the resulting solutions when solving well-known test functions. In the statistical analysis, the average, standard deviation, and minimum number of function evaluations are calculated for various levels of solution quality. In the graphical analysis, qualified run-length distributions are used to show the probability of solving a suite of well-known test functions at different levels of solution quality. The results demonstrate that MTO with climate change is able to reach the global solution for all the problems considered. In addition, this MTO variant generally requires fewer function evaluations than Particle Swarm Optimization and Bacterial Foraging to reach a solution of a given quality.