An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

Comparison of Websites Employing Search Engine Optimization and Live Data

This study compares websites that take live data into account using search engine optimization(SEO).A series of steps called search engine optimization can help a website rank highly in search engine results.Static websites and dynamic websites are two different types of websites.Static websites must have the necessary expertise in programming compatible with SEO.Whereas in dynamic websites,one can utilize readily available plugins/modules.The fundamental issue of all website holders is the lower level of page rank,congestion,utilization,and exposure of the website on the search engine.Here,the authors have studied the live data of four websites as the real-time data would indicate how the SEO strategy may be applied to website page rank,page difficulty removal,and brand query,etc.It is also necessary to choose relevant keywords on any website.The right keyword might assist to increase the brand query while also lowering the page difficulty both on and off the page.In order to calculate Off-page SEO,On-page SEO,and SEO Difficulty,the authors examined live data in this study and chose four well-known Indian university and institute websites for this study:www.caluniv.ac.in,www.jnu.ac.in,www.iima.ac.in,and www.iitb.ac.in.Using live data and SEO,the authors estimated the Off-page SEO,On-page SEO,and SEO Difficulty.It has been shown that the Off-page SEO of www.caluniv.ac.in is lower than that of www.jnu.ac.in,www.iima.ac.in,and www.iitb.ac.in by 9%,7%,and 7%,respectively.On-page SEO is,in comparison,4%,1%,and 1%more.Every university has continued to keep up its own brand query.Additionally,www.caluniv.ac.in has slightly less SEO Difficulty compared to other websites.The final computed results have been displayed and compared.

Engineering optimization approach of nonaqueous electrolyte for sodium ion battery with long cycle life and safety

Electrolyte design strategies are closely related to the capacities, cycle life and safety of sodium–ion batteries. In this study, we aimed to optimize electrolyte with the focus on engineering aspects. The basic physicochemical properties including ionic conductivity, viscosity,wettability and thermochemical stability of the electrolytes using Na PF6 as the solute and the mixed solvent with different components of EMC,DMC or DEC in PC or EC were systematically measured. Ah pouch cell with NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2)/hard carbon electrodes was used to evaluate the performance of the prepared electrolytes. By using the Inductive Coupled Plasma Emission Spectrometer(ICP), X-ray photoelectron spectroscopy(XPS), Thermogravimetric-differential scanning calorimetry(TG-DSC) and Accelerating Rate Calorimeter(ARC), we show that an optimized electrolyte can effectively promote the formation of a protective interfacial layer on two electrodes, which not only retards parasitic reactions between the electrodes and electrolyte but also suppresses dissolution of metal ions from the cathode. With an optimized electrolyte, a NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2)/hard carbon cell can attain 56.16% capacity retention under the low temperature of -40℃, and can be able to retain 80%capacity retention after more than 2500 cycles while presenting excellent thermal safety.

Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

The particle swarm optimization(PSO)algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and sh.PSO is essentially an unconstrained algorithm and requires constraint handling techniques(CHTs)to solve constrained optimization problems(COPs).For this purpose,we integrate two CHTs,the superiority of feasibility(SF)and the violation constraint-handling(VCH),with a PSO.These CHTs distinguish feasible solutions from infeasible ones.Moreover,in SF,the selection of infeasible solutions is based on their degree of constraint violations,whereas in VCH,the number of constraint violations by an infeasible solution is of more importance.Therefore,a PSO is adapted for constrained optimization,yielding two constrained variants,denoted SF-PSO and VCH-PSO.Both SF-PSO and VCH-PSO are evaluated with respect to ve engineering problems:the Himmelblau’s nonlinear optimization,the welded beam design,the spring design,the pressure vessel design,and the three-bar truss design.The simulation results show that both algorithms are consistent in terms of their solutions to these problems,including their different available versions.Comparison of the SF-PSO and the VCHPSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations used.We also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants.

Systems engineering optimization of Omid-2 in the conceptual design phase

Due to the importance and role of systems engineering in space mission developments, optimization of Omid＇s systems engineering as a milestone to its current and future generations is focused. In this regard systems engineering management organization as the basis of optimization work flow in the conceptual design phase is proposed for improvement. To improve the systems engineering management, an agile enhanced organization chart is developed that defines various system duties. This is a type of concurrent engineering approach that promotes direct communication and data interchange between the team members. Due to the importance of decision making in the conceptual design phase, two design matrices are constructed that portray merits of various design options in terms of improved satellite life as well as specific choices of remote sensing capability for the Omid second generation（Omid-2）. Conceptual design optimization is explored considering several structural objectives as well as optimal solar energy absorption utilizing a multiple criteria decision making approach. The Eigenvector method is utilized to formulate the objective function via expert judgment. This approach is robust with respect to designer probable miss-judgment. The optimized version of Omid-2 turned out to be a passive Z-axis spin stabilized satellite made of hexagonal honeycomb configuration with carbon-epoxy side panels and Aluminum bottom plate.

Search Engine Optimization,Competitive Advantage and Market Performance of Registered Tours and Travel Agencies in Nairobi City County,Kenya

COVID-19 is a devastating pandemic with widespread negative health,social,and economic consequences.Due to drastic changes in the business environment of tour and travel agencies,firms and marketing managers can now use search engine optimization to effectively position themselves.The study’s main goal is to evaluate the effect of search engine optimization on the market performance of registered tours and travel agencies in Nairobi County,Kenya.Kenya’s tourism ministry and state government work hard to improve the business climate for tour and travel companies.Despite the overall positive image,international tourist market growth rates in Kenya have been 3.5 percent slower from 2017 to 2019 compared to previous years.This was further aggregated by the onset of the COVID-19 pandemic in the year 2020,when the growth rate of tours and travel agencies fell by 65%.The study’s main goal is to evaluate the effect of search engine optimization on the market performance of registered tours and travel agencies in Nairobi County,Kenya.This study adopted a positivist philosophy.Both descriptive and explanatory research designs were used.A self-administered semi-structured questionnaire was used to collect data from 324 registered tours and travels agencies picked from and a sample of 179 were used.Data analysis included the development and interpretation of both descriptive and inferential statistics,such as frequencies,mean,percentages,and standard deviation,and was presented using tables and numerical values.The results of regression analysis established that search engine optimization had a positive and significant effect on market performance of the registered tours and travel agencies picked from a sample of 179.The study recommends that agency management ensure that the firm’s website is easily accessible in order to improve agency performance.Using the internet to gain a large market share can assist tours and travel agencies in improving the performance and income of their websites.

Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.

An optimization method: hummingbirds optimization algorithm

This paper introduces an optimization algorithm, the hummingbirds optimization algorithm(HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching phase and a guide-searching phase. With these two phases, the exploration and exploitation abilities of the algorithm can be balanced. Both the constrained and unconstrained benchmark functions are employed to test the performance of HOA. Ten classic benchmark functions are considered as unconstrained benchmark functions. Meanwhile, two engineering design optimization problems are employed as constrained benchmark functions. The results of these experiments demonstrate HOA is efficient and capable of global optimization.

Optimization of clay material mixture ratio and filling process in gypsum mine goaf

Because there is neither waste rock nor mill tailings in the gypsum mine, and the buildings on the goaf of gypsum mine are needed to be protected, the research proposed the scheme of the clay filling technology. Gypsum, cement, lime and water glass were used as adhesive, and the strength of different material ratios were investigated in this study. The influence factors of clay strength were obtained in the order of cement, gypsum, water glass and lime. The results show that the cement content is the determinant influence factor, and gypsum has positive effects, while the water glass can enhance both clay strength and the fluidity of the filing slurry. Furthermore, combining chaotic optimization method with neural network, the optimal ratio of composite cementing agent was obtained. The results show that the optimal ratio of water glass, cement, lime and clay (in quality) is 1.17:6.74:4.17:87.92 in the process of bottom self-flow filling, while the optimal ratio is 1.78:9.58:4.71:83.93 for roof-contacted filling. A novel filling process to fill in gypsum mine goaf with clay is established. The engineering practice shows that the filling cost is low, thus, notable economic benefit is achieved.

Damping Collaborative Optimization of Five-suspensions for Driver-seat-cab Coupled System

Both the seat and cab system of truck play a vital role in ride comfort.The damping matching methods of the two systems are studied separately at present.However,the driver,seat,and cab system are one inseparable whole.In order to further improve ride comfort,the seat suspension is regarded as the fifth suspension of the cab,a new idea of ＂Five-suspensions＂ is proposed.Based on this idea,a 4 degree-of-freedom driver-seat-cab coupled system model is presented.Using the tested cab suspensions excitations as inputs and seat acceleration response as compared output,the simulation model is built.Taking optimal ride comfort as target,a new method of damping collaborative optimization for Five-suspensions is proposed.With a practical example of seat and cab system,the damping parameters are optimized and validated by simulation and bench test.The results show the seat vertical frequency-weighted RMS acceleration values tested for the un-optimized and optimized Five-suspensions are 0.50 m/s～2 and 0.39 m/s～2,respectively,with a decrease by 22.0%,which proves the model and method proposed are correct and reliable.The idea of ＂Five-suspensions＂ and the method proposed provide a reference for achieving global optimal damping matching of seat suspension and cab suspensions.

Stability analysis of backflling in subsiding area and optimization of the stoping sequence

In underground mining by sublevel caving method, the deformation and damage of the surface induced by subsidence are the major challenging issues. The dynamic and soft backflling body increases the safety risks in the subsiding area. In this paper, taking Zhangfushan iron mine as an example, the ore body and the general layout are focused on the safety of backflling of mined-out area. Then, we use the ANSYS software to construct a three-dimensional(3D) model for the mining area in the Zhangfushan iron mine. According to the simulation results of the initial mining stages, the ore body is stoped step by step as suggested in the design. The stability of the backflling is back analyzed based on the monitored displacements, considering the stress distribution to optimize the stoping sequence. The simulations show that a reasonable stoping sequence can minimize the concentration of high compressive stress and ensure the safety of stoping of the ore body.

CQFFA:A Chaotic Quasi-oppositional Farmland Fertility Algorithm for Solving Engineering Optimization Problems

Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and exploitation.The literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these drawbacks.Therefore,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising solutions.Furthermore,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA algorithm.The improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification component.The results obtained show that the proposed CQFFA model has been significantly improved.It is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and visually.Also,the CQFFA algorithm has evaluated six real-world engineering problems.The experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.

Writing strategies for improving the access of medical literature

When conducting a literature review,medical authors typically search for relevant keywords in bibliographic databases or on search engines like Google.After selecting the most pertinent article based on the title’s relevance and the abstract’s content,they download or purchase the article and cite it in their manuscript.Three major elements influence whether an article will be cited in future manuscripts:the keywords,the title,and the abstract.This indicates that these elements are the“key dissemination tools”for research papers.If these three elements are not determined judiciously by authors,it may adversely affect the manuscript’s retrievability,readability,and citation index,which can negatively impact both the author and the journal.In this article,we share our informed perspective on writing strategies to enhance the searchability and citation of medical articles.These strategies are adopted from the principles of search engine optimization,but they do not aim to cheat or manipulate the search engine.Instead,they adopt a reader-centric content writing methodology that targets well-researched keywords to the readers who are searching for them.Reputable journals,such as Nature and the British Medical Journal,emphasize“online searchability”in their author guidelines.We hope that this article will encourage medical authors to approach manuscript drafting from the perspective of“looking inside-out.”In other words,they should not only draft manuscripts around what they want to convey to fellow researchers but also integrate what the readers want to discover.It is a call-to-action to better understand and engage search engine algorithms,so they yield information in a desired and self-learning manner because the“Cloud”is the new stakeholder.

Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for Engineering Problems

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more complex.Many novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this problem.Among them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization performance.However,it tends to stagnate prematurely to local optima in solving some specific problems.Therefore,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called CGSE.In this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of SE.To objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with constraints.The final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real applications.Therefore,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

水下传感网络的低复杂度APIT算法及OPNET仿真实现

由于水声传感网络具有能量的局限性,所以低复杂度的定位算法更适用于水声传感网络。传统的APIT算法能够以较少的控制开销获得较好的定位精度,有利于水下传感网络定位的实现,但其复杂度高,冗余误差较大。以点扫描的方式取代传统网格扫描法,提出一种低复杂度的APIT算法,并在OPNET平台上搭建水声传感网络环境,阐述该算法在水下传感网络节点定位的实现过程。仿真结果表明,待定位节点与锚节点密度的增加有助于改善算法的性能,且在同等条件下本文算法比传统APIT算法定位精度更高。

The High Value CVT Concept--Cost Effective and Powerful

Based on the comprehensive comparison of vehicle performance in economy,engine power,driving smoothness,and efficiency cost as well as pollutant emission etc,the paper discussed the high value CVT concept from an angle of the cost effective and powerful for vehicle.In the paper,it researched the related technical detail in CVT.By means of realizing the continuous change in transmission ratio,it could obtain the optimal matching between transmission system and engine operating mode,and enhance the characteristic of fuel oil in economy,and also improve the convenience in manipulation for driver and make passenger comfortable.For easy to understand the concept,the paper made the comparison analysis in many aspects such as performance,transmission specification,high value CVT hybrid,orifice torque sensor,hydraulic system,high value CVT em,new chain portfolio and assessment of the high value CVT on the NEDC.Finally it showed the potential advantages of CVT technology development,and proposed future developing trends to realize technical scheme of high value CVT.

Recent advances in microbial production of phenolic compounds

Phenolic compounds(PCs)are a group of compounds with various applications in nutraceutical,pharmaceutical and cosmetic industries.Their supply by plant extraction and chemical synthesis is often limited by low yield and high cost.Microbial production represents as a promising alternative for efficient and sustainable production of PCs.In this review,we summarize recent advances in this field,which include enzyme mining and engineering to construct artificial pathways,balance of enzyme expression to improve pathway efficiency,coculture engineering to alleviate metabolic burden and side-reactions,and the use of genetic circuits for dynamic regulation and high throughput screening.Finally,current challenges and future perspectives for efficient production of PCs are also discussed.

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 of helicopter autorotation landing following engine failure based on a six-degree-of-freedom rigid-body dynamic model

This paper focuses on the prediction of the safe autorotation landing operations of a helicopter following engine failure.The autorotation landing procedure is formulated as a nonlinear optimal control problem based on an augmented six-degree-of-freedom rigid-body flight dynamic model.First,the cost function and constraints are properly selected.The direct transcription approach is then employed to solve the optimal control problem.For a UH-60 helicopter,the optimal solutions with the rigid-body model are compared with those obtained using a two-dimensional point-mass model.It is found that the optimal solutions using the two different models show reasonably good agreement,and furthermore the optimal solutions using the rigid-body model involve the time histories of angular rates and attitudes,lateral velocity and position,as well as pitch controls.Finally the optimal control formulations with different cost functions are proposed for taking account of 1-s time delay and minimum touchdown speed.The calculated control strategies and trajectories are realistic.

Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.