A Metaheuristic Algorithm Based on Chemotherapy Science: CSA

Among scientific fields of study, mathematical programming has high status and its importance has led researchers to develop accurate models and effective solving approaches to addressing optimization problems. In particular, metaheuristic algorithms are approximate methods for solving optimization problems whereby good (not necessarily optimum) solutions can be generated via their implementation. In this study, we propose a population-based metaheuristic algorithm according to chemotherapy method to cure cancers that mainly search the infeasible region. As in chemotherapy, Chemotherapy Science Algorithm (CSA) tries to kill inappropriate solutions (cancers and bad cells of the human body); however, this would inevitably risk incidentally destroying some acceptable solutions (healthy cells). In addition, as the cycle of cancer treatment repeats over and over, the algorithm is iterated. To align chemotherapy process with the proposed algorithm, different basic terms and definitions including Infeasibility Function (IF), objective function (OF), Cell Area (CA), and Random Cells (RCs) are presented in this study. In the terminology of algorithms and optimization, IF and OF are mainly applicable as criteria to compare every pair of generated solutions. Finally, we test CSA and its structure using the benchmark Traveling Salesman Problem (TSP).