Constrained optimization based on hybrid version of superiority of feasibility solution strategy

To solve constrained optimization problems (COPs), teaching learning-based optimization (TLBO) has been used in this study as a baseline algorithm. Different constraint handling techniques (CHTs) are incorporated in the framework of TLBO. The superiority of feasibility (SF) is one of the most commonly used and much effective CHTs with various decisive factors. The most commonly utilized decision-making factors in SF are a number of constraints violated (NCV) and weighted mean (WM) values for comparing solutions. In this paper, SF based on a number of constraints violated (NCVSF) and weighted mean (WMSF) is incorporated in the framework of TLBO and applied on CEC-2006 constrained benchmark functions. The use of a single factor for making the decision of the winner might be not a good idea. The combined use of NCV and WM factors in hybrid superiority of feasibility (HSF) has shown the dominating role of NCV over WM. We have employed NCVSF, WMSF, and HSF in the TLBO framework and suggested three constrained versions, namely NCVSF-TLBO, WMSF-TLBO, and HSF-TLBO. The performance of the proposed algorithms is evaluated upon CEC-2006 constrained benchmark functions. Among them, HSF-TLBO has shown better performance on most of the used constrained optimization problems in terms of proximity and diversity.