Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

Abstract

Bearing is one of the most fundamental components of rotary machinery, and its fatigue life is a crucial factor in designing. The design optimization of tapered roller bearing (TRB) is a complex design problem because various arrays of designing parameters and functional requirements should be fulfilled. Since there are many design variables and nonlinear constraints, presenting an optimal design of TRBs poses some challenges for metaheuristic algorithms. The Harris hawks optimization (HHO) algorithm is a robust nature-inspired method with unique exploitation and exploration phases due to its time-varying structure. However, this metaheuristic algorithm may still converge to local optima for more challenging problems such as the design of TRBs. Therefore, this study aims to improve the accuracy and efficiency of the shortcomings of this algorithm. The performance of the proposed algorithm is first evaluated for the TRB optimization problem. The TRB optimization design has nine design variables and 26 constraints because of geometrical dimensions and strength conditions. The productivity of the proposed method is compared with diverse metaheuristic algorithms in the literature. The results demonstrate the significant development of dynamic load capacity in comparison to the standard value. Furthermore, the enhanced version of the HHO algorithm presented in this study is benchmarked with various well-known engineering problems. For supplementary materials regarding algorithms in this research, readers can refer to https://aliasgharheidari.com .

A mixed Harris hawks optimization algorithm based on the pinhole imaging strategy for solving numerical optimization problems

“…The quantum particle-enhanced multiple Harris hawks algorithm proposed by Gölcük et al [26] solved the dynamic optimization problem, and it was designed as a multi-population algorithm, which could further process the possible multiple optimal solutions. The multi-strategy Harris hawks optimization algorithm proposed by Abbasi et al [27] was used to solve the tapered roller bearing design problem, which confirmed the effectiveness of the algorithm under complex and variable constraints. The improved HHO algorithm proposed by Hamza et al [28] was used to optimize the prioritization of software testing, and the experimental results highlighted the high efficiency of the MHHO-TCP technique.…”

A mixed Harris hawks optimization algorithm based on the pinhole imaging strategy for solving numerical optimization problems

“…The quantum particle-enhanced multiple Harris hawks algorithm proposed by Gölcük et al [26] solved the dynamic optimization problem, and it was designed as a multi-population algorithm, which could further process the possible multiple optimal solutions. The multi-strategy Harris hawks optimization algorithm proposed by Abbasi et al [27] was used to solve the tapered roller bearing design problem, which confirmed the effectiveness of the algorithm under complex and variable constraints. The improved HHO algorithm proposed by Hamza et al [28] was used to optimize the prioritization of software testing, and the experimental results highlighted the high efficiency of the MHHO-TCP technique.…”

Multi-strategy hybrid heuristic algorithm for single container weakly heterogeneous loading problem

“…In (Peng et al, 2021b), Peng et al proposed a multiple strategy serial framework to improve cuckoo search algorithm to avoid the local optimum due to its original unitary search strategy. In (Abbasi et al, 2021), Abbasi et al introduced a chaotic initialization approach and several updated besiege strategies to improve Harris Hawk optimization (HHO) method for the optimal design of tapered roller bearings. Similarly, in (Li et al, 2021a), Li et al presented to use logarithmic spiral and opposition-based learning and a local search technique to improve the global convergence of HHO.…”

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