Using Chemotherapy Science Algorithm (CSA) to Solve the Knapsack Problem

Optimization, which, by definition, can help one find the best solution from all feasible solutions, has sometimes been an interesting and important area for research in science. Solving real and hard optimization problems calls for developing approximate, heuristic, and meta-heuristic algorithms. In this article, a new meta-heuristic algorithm is proposed on the basis of the chemotherapy method to cure cancers - this algorithm mainly searches the infeasible region. As in chemotherapy, this algorithm tries to kill unsatisfactory (especially infeasible) solutions (cancers cells); however, collateral damage is sometimes inevitable - some healthy, innocuous, and good cells might be targeted as well. Also, different conceptual terms including Cell Size, Cell Position, Cell Area, and Random Cells are presented and defined in this article. Furthermore, Chemotherapy Science Algorithm (CSA) and its structure are tested based on benchmark Knapsack Problem. Reported results show the efficiency of the proposed algorithm.