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SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization

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  • A. S. Syed Shahul Hameed

    (Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal 609609, India)

  • Narendran Rajagopalan

    (Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal 609609, India)

Abstract

Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party searching for a treasure. Motivated by the search party’s behavior, we abstract the “Divide, Conquer, Assemble” (DCA) approach. The DCA approach allows us to parallelize the traditional gradient descent algorithm in a strikingly simple manner. Essentially, multiple gradient descent instances with different learning rates are run parallelly, periodically sharing information. We call it the search party gradient descent (SPGD) algorithm. Experiments performed on a diverse set of classical benchmark functions show that our algorithm is good at optimizing. We believe our algorithm’s apparent lack of complexity will equip researchers to solve problems efficiently. We compare the proposed algorithm with SciPy’s optimize library and it is found to be competent with it.

Suggested Citation

  • A. S. Syed Shahul Hameed & Narendran Rajagopalan, 2022. "SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization," Mathematics, MDPI, vol. 10(5), pages 1-24, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:800-:d:762917
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    References listed on IDEAS

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    2. Hu-Sheng Wu & Feng-Ming Zhang, 2014. "Wolf Pack Algorithm for Unconstrained Global Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-17, March.
    3. Ferreiro, Ana M. & García-Rodríguez, José Antonio & Vázquez, Carlos & e Silva, E. Costa & Correia, A., 2019. "Parallel two-phase methods for global optimization on GPU," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 67-90.
    4. Wali Khan Mashwani, 2013. "Comprehensive Survey of the Hybrid Evolutionary Algorithms," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 4(2), pages 1-19, April.
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