Sait, Sadiq M.Bureerat, SujinPholdee, Nantiwai2022-11-282022-11-282019-08Yıldız, A. R. vd. (2019). ''A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems''. Materials Testing, 61(8), 735-743.0025-53002195-8572https://doi.org/10.3139/120.111378https://www.degruyter.com/document/doi/10.3139/120.111378/htmlhttp://hdl.handle.net/11452/29604In 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.eninfo:eu-repo/semantics/closedAccessHarris hawks algorithmNelder meadHybrid optimizationMillingdesignParticle swarm optimizationOptimal machining parametersSurface grinding processMultiobjective optimizationStructural optimizationMemetic agorithmsDifferential evolutionGlobal optimizationMilling operationsGenetic algorithmAnt colony optimizationDesignGenetic algorithmsLearning algorithmsMilling (machining)Simulated annealingArtificial bee colony algorithmsGravitational search algorithmsHybrid optimizationNelder meadsOptimization of process parametersTeaching-learning-based optimizationsManufactureAnt colony optimizationDesignGenetic algorithmsLearning algorithmsMilling (machining)Simulated annealingGravitational search algorithmsHybrid optimizationNelder meadsOptimization of process parametersParticle swarm optimization algorithmSimulated annealing algorithmsTeaching-learning-based optimizationsArticle0004787599000042-s2.0-85072323937735743618Materials science, characterization & testingCutting Process; Chatter; Turning