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A Metaheuristic Algorithm Based on Chemotherapy Science: CSA

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  • Mohammad Hassan Salmani
  • Kourosh Eshghi

Abstract

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).

Suggested Citation

  • Mohammad Hassan Salmani & Kourosh Eshghi, 2017. "A Metaheuristic Algorithm Based on Chemotherapy Science: CSA," Journal of Optimization, Hindawi, vol. 2017, pages 1-13, February.
    • Handle: RePEc:hin:jjopti:3082024
    DOI: 10.1155/2017/3082024
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    References listed on IDEAS

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    1. Mohammad Hassan Salmani & Kourosh Eshghi, 2017. "A Smart Structural Algorithm (SSA) Based on Infeasible Region to Solve Mixed Integer Problems," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 8(1), pages 24-44, January.
    2. Rubinstein, Reuven Y., 1997. "Optimization of computer simulation models with rare events," European Journal of Operational Research, Elsevier, vol. 99(1), pages 89-112, May.
    3. Omid Haddad & Abbas Afshar & Miguel Mariño, 2006. "Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(5), pages 661-680, October.
    4. Roberto Battiti & Giampietro Tecchiolli, 1994. "The Reactive Tabu Search," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 126-140, May.
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    Cited by:

    1. Ashish Kumar Singh & Anoj Kumar, 2023. "An improved dynamic weighted particle swarm optimization (IDW-PSO) for continuous optimization problem," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 404-418, March.

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