A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems

Author:

Panagant Natee1,Yıldız Mustafa2,Pholdee Nantiwat1,Yıldız Ali Rıza2,Bureerat Sujin1,Sait Sadiq M.3

Affiliation:

1. Khon Kaen Thailand

2. Bursa Turkey

3. Dhahran Saudi Arabia

Abstract

Abstract The marine predators optimization algorithm (MPA) is a recently developed nature-inspired algorithm. In this paper, the Nelder-Mead algorithm is utilized to improve the local exploitation powers of the MPA when described as a hybrid marine predators and Nelder-Mead (HMPANM). Due to the harsh competitive conditions as well as the transition to new vehicles such as hybrid and full-electrical cars, the interest in the design of light and low-cost vehicles is increasing. In this study, a recent metaheuristic addition, a hybrid marine predators optimization algorithm, is used to solve a structural design optimization problem to prove how the HMPANM can be used in solving industrial design problems. The results strongly prove the capability of the HMPANM for the optimum design of components in the automotive industry.

Publisher

Walter de Gruyter GmbH

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

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1. Marine predators algorithm: A comprehensive review;Machine Learning with Applications;2023-06

2. Marine Predators Algorithm: A Review;Archives of Computational Methods in Engineering;2023-04-19

3. A Comparative Study of State-of-the-art Metaheuristics for Solving Many-objective Optimization Problems of Fixed Wing Unmanned Aerial Vehicle Conceptual Design;Archives of Computational Methods in Engineering;2023-04-11

4. Marine predator algorithm with elite strategies for engineering design problems;Concurrency and Computation: Practice and Experience;2023-01-12

5. A novel balanced Aquila optimizer using random learning and Nelder–Mead simplex search mechanisms for air–fuel ratio system control;Journal of the Brazilian Society of Mechanical Sciences and Engineering;2023-01

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