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Journal of Optimization
Volume 2017, Article ID 3082024, 13 pages
Research Article

A Metaheuristic Algorithm Based on Chemotherapy Science: CSA

Department of Industrial Engineering, Sharif University of Technology, Tehran 11365/8639, Iran

Correspondence should be addressed to Mohammad Hassan Salmani; moc.liamg@inamlashm

Received 3 June 2016; Revised 29 November 2016; Accepted 15 January 2017; Published 23 February 2017

Academic Editor: Bijaya Ketan Panigrahi

Copyright © 2017 Mohammad Hassan Salmani and Kourosh Eshghi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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).