Optimizing Bucket Elevator Performance through a Blend of Discrete Element Method, Response Surface Methodology, and Firefly Algorithm Approaches

This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.

Quantum fireworks algorithm for optimal cooperation mechanism of energy harvesting cognitive radio

For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting(EH) cognitive radio(CR). Considering the CR system with EH and cooperative relay, a best cooperative mechanism(BCM)is proposed for CR with EH. To get the optimal estimation performance, a quantum fireworks algorithm(QFA) is designed to resolve the difficulties of maximal throughput and EH, and the proposed cooperative mechanism is called as QFA-BCM. The proposed QFA combines the advantages of quantum computation theory with the fireworks algorithm(FA). Thus the QFA is able to obtain the optimal solution and its convergence performance is proved. By using the new cooperation mechanism and computing algorithm, the proposed QFA-BCM method can achieve comparable maximal throughput in the new timeslot structure. Simulation results have proved that the QFA-BCM method is superior to previous non-cooperative and cooperative mechanisms.

University's Dynamic Performance Appraisal System Based on Data

Teachers are key participants in universities,and the performance appraisal of teacher is an important part of college work.By analyzing the data of behavior generated by different departments in university,analytic hierarchy process(AHP) is used to establish the preliminary library of performance indicators for teachers,and the correlation among all the performance indicators is inspected by using data mining method at this time.On this basis,a more objective,comprehensive and scientific performance appraisal system is constructed through principal components analysis(PCA),which is more suitable for university itself.Finally,in order to solve the problems existed in current performance appraisal system,a dynamic evaluation model is put forward by regulating the weight of indicator according to the historical data,highlighting the continuity of the system.

Firefly algorithm with division of roles for complex optimal scheduling

A single strategy used in the firefly algorithm(FA)cannot effectively solve the complex optimal scheduling problem.Thus,we propose the FA with division of roles(DRFA).Herein,fireflies are divided into leaders,developers,and followers,while a learning strategy is assigned to each role:the leader chooses the greedy Cauchy mutation;the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development;the follower randomly selects two excellent particles for global exploration.To improve the efficiency of the fixed step size used in FA,a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages.Role division can balance the development and exploration ability of the algorithm.The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems.The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

Enhancing Firefly Algorithm with Best Neighbor Guided Search Strategy

Firefly algorithm(FA)is a recently-proposed swarm intelligence technique.It has shown good performance in solving various optimization problems.According to the standard firefly algorithm and most of its variants,a firefly migrates to every other brighter firefly in each iteration.However,this method leads to defects of oscillations of positions,which hampers the convergence to the optimum.To address these problems and enhance the performance of FA,we propose a new firefly algorithm,which is called the Best Neighbor Firefly Algorithm(BNFA).It employs the best neighbor guided strategy,where each firefly is attracted to the best firefly among some randomly chosen neighbors,thus reducing the firefly oscillations in every attraction-induced migration stage,while increasing the probability of the guidance a new better direction.Moreover,it selects neighbors randomly to prevent the firefly form being trapped into a local optimum.Extensive experiments are conducted to find out the optimal parameter settings.To verify the performance of BNFA,13 classical benchmark functions are tested.Results show that BNFA outperforms the standard FA and other recently proposed modified FAs.

A Hybrid Firefly Algorithm for Optimizing Fractional Proportional-Integral-Derivative Controller in Ship Steering

In this paper, a new algorithm which integrates the powerful firefly Mgorithm （FA） and the ant colony optimization （ACO） has been used in tracking control of ship steering for optimization of fractional-order proportional-integral-derivative （FOPID） controller gains. Particle swarm optimization （PSO） algorithm is also used to optimize FOPID controllers, and their performances are compared. It is found that FA optimized FOPID controller gives better performance than others. Sensitivity analysis has been carried out to see the robustness of optimum FOPID gains obtained at nominal conditions to wide changes in system parameters, and the optimum FOPID gains need not be reset for wide changes in system parameters.

Coal and gas outburst prediction model based on principal component analysis and improved support vector machine

In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.