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A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies

Author

Listed:
  • Hernán Peraza-Vázquez
  • Adrián F. Peña-Delgado
  • Gustavo Echavarría-Castillo
  • Ana Beatriz Morales-Cepeda
  • Jonás Velasco-Álvarez
  • Fernando Ruiz-Perez

Abstract

A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA’s performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.

Suggested Citation

  • Hernán Peraza-Vázquez & Adrián F. Peña-Delgado & Gustavo Echavarría-Castillo & Ana Beatriz Morales-Cepeda & Jonás Velasco-Álvarez & Fernando Ruiz-Perez, 2021. "A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-19, September.
    • Handle: RePEc:hin:jnlmpe:9107547
    DOI: 10.1155/2021/9107547
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    Cited by:

    1. Liu, Xiaomei & Li, Sihan & Gao, Meina, 2024. "A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast," Energy, Elsevier, vol. 296(C).
    2. Weihong Cai & Fengxi Duan, 2023. "Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm," Future Internet, MDPI, vol. 15(11), pages 1-23, October.
    3. Weng-Hooi Tan & Junita Mohamad-Saleh, 2023. "Critical Review on Interrelationship of Electro-Devices in PV Solar Systems with Their Evolution and Future Prospects for MPPT Applications," Energies, MDPI, vol. 16(2), pages 1-37, January.
    4. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
    5. Wen-Cheng Wang & Ngakan Ketut Acwin Dwijendra & Biju Theruvil Sayed & José Ricardo Nuñez Alvarez & Mohammed Al-Bahrani & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    6. Dahu Li & Hongyu Zhou & Yuan Chen & Yue Zhou & Yuze Rao & Wei Yao, 2023. "A Frequency Support Approach for Hybrid Energy Systems Considering Energy Storage," Energies, MDPI, vol. 16(10), pages 1-16, May.
    7. Ziyan Zhao & Pengkai Xiao & Jiacun Wang & Shixin Liu & Xiwang Guo & Shujin Qin & Ying Tang, 2023. "Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time," Mathematics, MDPI, vol. 12(1), pages 1-19, December.

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