A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems
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Date
2019-08
Journal Title
Journal ISSN
Volume Title
Publisher
Walter de Gruyter
Abstract
In this paper, a novel hybrid optimization algorithm (H-HHONM) which combines the Nelder-Mead local search algorithm with the Harris hawks optimization algorithm is proposed for solving real-world optimization problems. This paper is the first research study in which both the Harris hawks optimization algorithm and the H-HHONM are applied for the optimization of process parameters in milling operations. The H-HHONM is evaluated using well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a milling manufacturing optimization problem is solved for investigating the performance of the H-HHONM. Additionally, the salp swarm algorithm is used to solve the milling problem. The results of the H-HHONM for design and manufacturing problems solved in this paper are compared with other optimization algorithms presented in the literature such as the ant colony algorithm, genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, artificial bee colony algorithm, teaching learning-based optimization algorithm, cuckoo search algorithm, multi-verse optimization algorithm, Harris hawks optimization optimization algorithm, gravitational search algorithm, ant lion optimizer, moth-flame optimization algorithm, symbiotic organisms search algorithm, and mine blast algorithm. The results show that H-HHONM is an effective optimization approach for optimizing both design and manufacturing optimization problems.
Description
Keywords
Harris hawks algorithm, Nelder mead, Hybrid optimization, Millingdesign, Particle swarm optimization, Optimal machining parameters, Surface grinding process, Multiobjective optimization, Structural optimization, Memetic agorithms, Differential evolution, Global optimization, Milling operations, Genetic algorithm, Ant colony optimization, Design, Genetic algorithms, Learning algorithms, Milling (machining), Simulated annealing, Artificial bee colony algorithms, Gravitational search algorithms, Hybrid optimization, Nelder meads, Optimization of process parameters, Teaching-learning-based optimizations, Manufacture, Ant colony optimization, Design, Genetic algorithms, Learning algorithms, Milling (machining), Simulated annealing, Gravitational search algorithms, Hybrid optimization, Nelder meads, Optimization of process parameters, Particle swarm optimization algorithm, Simulated annealing algorithms, Teaching-learning-based optimizations
Citation
Yıldız, A. R. vd. (2019). ''A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems''. Materials Testing, 61(8), 735-743.