Research Article A Metaheuristic Algorithm Based on Chemotherapy Science: CSA Mohammad Hassan Salmani and Kourosh Eshghi Department of Industrial Engineering, Sharif University of Technology, Tehran 11365/8639, Iran Correspondence should be addressed to Mohammad Hassan Salmani; mhsalmani@gmail.com 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. Tis 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 scientifc felds of study, mathematical programming has high status and its importance has led researchers to develop accurate models and efective 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, diferent basic terms and defnitions 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). 1. Introduction In the past few decades, various approaches have been proposed to solve optimization problems in two parts of exact and approximate methods. Te exact ones such as dynamic programming and branch and bound algorithms are only applicable to small-scale hard problems while for solving large-scale models and highly nonlinear optimization heuris- tic approaches should be applied [1]. Terefore, the need to provide efective approximate solving procedures named metaheuristic algorithms is known to every researcher. It is claimed that a metaheuristic algorithm far surpasses the heuristic one as the latter is just applicable for solving a special class of problems while one can implement the former for a wide range of mathematical models and optimization prob- lems. Te majority of the proposed metaheuristic algorithms in the literature are nature-inspired with stochastic behavior which can be categorized into two groups of population- based and single point search ones. Nature is of course a great and immense source of inspiration for solving hard and complex problems in computer science since it exhibits extremely diverse, dynamic, robust, complex, and fascinating phenomena [2]. It always fnds the optimal solution to solve its problem, maintaining a perfect balance among its components. As a matter of fact, nature provides some efcient ways for solving problems via ofering efcient methods to address mathematical models. Ant Colony Optimization (ACO), Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are the most well-known nature-inspired ones for solving optimization problems. Like these methods, this study also attempts to propose a natured- based metaheuristic algorithm whose origin is in chemother- apy cancer treatment. Chemotherapy (sometimes called “chemo”) uses more than 100 strong chemical drugs to treat cancer in a cycle and repetitive procedure, which is ofen used as the last resort to prevent the cancer from spreading, slow the cancer’s growth, kill cancer cells that may have spread to other parts of the body, relieve symptoms such as pain or blockages caused by Hindawi Journal of Optimization Volume 2017, Article ID 3082024, 13 pages https://doi.org/10.1155/2017/3082024