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SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Author

Listed:
  • Umesh Balande

    (Department of CSE, Visvesvaraya National Institute of Technology, Nagpur 440010, India)

  • Deepti Shrimankar

    (Department of CSE, Visvesvaraya National Institute of Technology, Nagpur 440010, India)

Abstract

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

Suggested Citation

  • Umesh Balande & Deepti Shrimankar, 2019. "SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems," Mathematics, MDPI, vol. 7(3), pages 1-26, March.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:3:p:250-:d:212761
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    References listed on IDEAS

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    1. Ali Wagdy Mohamed, 2018. "A novel differential evolution algorithm for solving constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 659-692, March.
    2. Nikhil Padhye & Pulkit Mittal & Kalyanmoy Deb, 2015. "Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization," Computational Optimization and Applications, Springer, vol. 62(3), pages 851-890, December.
    3. Kvasov, Dmitri E. & Mukhametzhanov, Marat S., 2018. "Metaheuristic vs. deterministic global optimization algorithms: The univariate case," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 245-259.
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    Cited by:

    1. Jing Wang & Huafei Sun & Simone Fiori, 2019. "Empirical Means on Pseudo-Orthogonal Groups," Mathematics, MDPI, vol. 7(10), pages 1-20, October.

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