An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems

Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Navigation Optimizer Algorithm (QANA) is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds. Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields. However, it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems. Therefore, instead of proposing a new algorithm to solve these weaknesses, we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA. The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm (HQANA) is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions, 30, 50, and 100. The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms, including PSO, KH, GWO, WOA, CSA, HOA, BO, and QANA. Ultimately, five constrained engineering optimization problems from the latest test suite, CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems. The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.

Study,Analysis and Comparison between Amazon A10 and A11 Search Algorithm

The entirety of Amazon’s sales being powered by Amazon Search,one of the leading e-commerce platforms around the globe.As a result,even slight boosts in appropriateness can have a major impact on profits as well as the shopping experience of millions of users.Throughout the beginning,Amazon’s product search engine was made up of a number of manually adjusted ranking processes that made use of a limited number of input features.Since that time,a significant amount has transpired.Many people overlook the fact that Amazon is a search engine,and even the biggest one for e-commerce.It is indeed time to begin treating Amazon truly as the top e-commerce search engine across the globe because it currently serves 54%of all product queries.In this paper,the authors have considered two most important Amazon search engine algorithms viz.A10 and A11 and comparative study has been discussed.

Engineering the Divide-and-Conquer Closest Pair Algorithm

We improve the famous divide-and-conquer algorithm by Bentley and Shamos for the planar closest-pair problem. For n points on the plane, our algorithm keeps the optimal O（n log n） time complexity and, using a circle-packing property, computes at most 7n/2 Euclidean distances, which improves Ge et al.＇s bound of （3n log n）/2 Euclidean distances. We present experimental results of our comparative studies on four different versions of the divide-and-conquer closest pair algorithm and propose two effective heuristics.

Semi-Empirical, Multi-Wavelength Raman Gain Engineering Algorithm with Pump Interaction Compensation Technique

We propose a simple algorithm for the precise engineering of multi-channel gain profile of Raman amplifier. By employing a linear approximation in the pump interaction calculation, together with a semi-empirical pump power correction using the simplistic output signal spectrum measurement, excellent gain engineering capability has been demonstrated for various target gain profiles, within +/- 0.4dB of error.

Fatigue Life Prediction for Flange Connecting Bolts of Wind Turbine Tower

Flange joint part is the weak link of wind turbine tower.In view of the special structure,complex stress and easy failure of the connecting bolt of the wind turbine tower flange,the relationship between the external load of the tower section and the internal stress of the bolt is established by the finite element method,and the time series internal stress of the bolt is calculated by the Schmidt-Neuper algorithm.The S-N curve which is suitable for the connecting bolt material of the tower flange is selected by the GL2010 specification.On the basis of Miner’s fatigue cumulative damage theory and rain flow counting method,the fatigue strength of the whole ring bolt is roughly calculated,and the most dangerous part is determined.The axial symmetry model of screw connection is used for accurately calculating the fatigue cumulative damage of the bolt at the dangerous part.The results show that the fatigue life of the bolts in the most dangerous position can meet the requirements,the engineering algorithm has advantages in determining the dangerous part of the whole ring bolt,and the finite element method has high accuracy in predicting the fatigue life of the bolts in the dangerous part.The proposed method is feasible and effective in predicting the fatigue life of the flange joint bolts of the tower.