A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems

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.

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

“…Since proposed [53], it has been used widely [54]- [56], such as solar energy [57]- [59], feature selection [60], drug design and discovery [61]. Furthermore, a large number of improved HHO variants have been presented, for example, hybrid HHO-based sine cosine mechanism [62], Nelder-mead driven HHO [63], generalized Gaussian distribution HHO [64], multi-objective HHO [65], mutation strategies-based HHO [66], diversification enriched HHO [58], Multi-population version [67] random forest model based-HHO [68]. In this study, the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer have been integrated to enhance and restore the search capability of the HHO.…”

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

“…Local optima avoidance and smooth transition from exploration to exploitation are among the major advantages of the HHO algorithm. According to these behaviors, the HHO has been applied to several global optimization and real-world engineering problems, including image segmentation [18,19], feature selection [20], spatial assessment of landslide susceptibility [21], parameter estimation of photovoltaic cells [22], image denoising [23], and others [24][25][26][27][28][29]. However, the HHO suffers from some limitations that affect its performance such that its exploration ability is weaker than its exploitation ability, and this leads to degradation of the performance of convergence and the quality of the solution.…”

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

“…Heidari et al [36] demonstrated that HHO outperforms nature-inspired techniques in 29 engineering problems. It had been used in various applications, such as feature selection [37], engineering problems [38][39][40][41][42][43], satellite image processing [44], prediction models [45], and scheduling tasks in cloud computing [46]. SSA is also a nature-inspired method that simulates the behavior of Salpidae's family.…”