A new metaheuristic algorithm for global optimization over continuous search space

A new metaheuristic global optimization method for non-linear and nondifferentiable problems is proposed and described in this article It is a swarm-based method which uses spherical boundaries in a vector search-space to explore for the optimal solution Having a few numbers of parameters to be adjusted being robust and fast needing less memory storage size and capability of escaping from local optima are the main features of this new method To analyze and evaluate the capability of this novel method ten benchmark functions are chosen and the results are compared with two existing optimization methods which are Differential Evolution and Particle Swarm Optimization Comparisons are made based on the consistency in obtaining optimal solutions computation time and convergence profile Results show the capability of the proposed method in finding a proper and fast solution and also escaping from local optima of the problem solution-space.