Space Net Optimization for Single Objective Bound Constrained Problems

Metaheuristic algorithms are capable of solving various complex engineering optimization problems efficiently; however, most of them rely on a certain number of searched solutions to guide their searches during the convergence process. Although it is not hard to imagine that a larger number of searched solutions will provide a better description of the landscape of the solution space, retaining all the searched solutions remains a challenge. Inspired by this observation, we present in this paper a novel called, which is equipped with a new mechanism called space net for depicting the landscape of the solution space using the information provided by most searched solutions. With the space net, a is then kind of like having a “vision” for knowing the landscape of the solution space to improve its search accuracy, such as investing more searches on high potential regions and reducing the number of searches on low potential regions. Simulation results show that is capable of providing a promising result for solving single objective bound constrained problems.