Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

Optimal Location and Sizing ofMulti-Resource Distributed Generator Based onMulti-Objective Artificial Bee Colony Algorithm

Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.

The improved artificial bee colony algorithm for mixed additive and multiplicative random error model and the bootstrap method for its precision estimation

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model.The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation.The experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation.The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

An improved bearing fault detection strategy based on artificial bee colony algorithm

The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.

Codebook design using improved particle swarm optimization based on selection probability of artificial bee colony algorithm

In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles.

Service Composition Instantiation Based on Cross-Modified Artificial Bee Colony Algorithm

Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified Artificial Bee Colony Algorithm(CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy. Firstly, web service instantiation model was established. What is more, to overcome the problem of discrete and chaotic solution space, the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm(GA). The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms.

A Discrete Multi‑Objective Artificial Bee Colony Algorithm for a Real‑World Electronic Device Testing Machine Allocation Problem

With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for electronic devices.Electronic device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the market.Considering the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved.This study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine allocation.First,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines.Second,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,respectively.Numerical experiments were conducted to evaluate the performance of the proposed algorithm.The experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency modules.The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.

Application of artificial bee colony algorithm in Rayleigh wave inversion

In order to solve the problems of multi-parameter,multi-extreme and multi-solution in the nonlinear iterative optimization process of Rayleigh wave inversion,the artificial bee colony(ABC)algorithm is selected for global nonlinear inversion.The global nonlinear inversion method does not rely on a strict initial model and does not need to calculate the derivative of the objective function.The ABC algorithm uses the local optimization behavior of each individual artificial bee to finally highlight the global optimal value in the colony,and the convergence speed is faster.While searching for the global optimal solution,an effective local search can also be performed to ensure the reliability of the inversion results.This paper uses the ABC algorithm to perform Rayleigh wave dispersion inversion on the actual seismic data to obtain a clear undergrounding of shear wave velocity profile and accurately identify the location of the high-velocity interlayer.It is verified that the ABC algorithm used in the inversion of the Rayleigh wave dispersion curve is stable and converges quickly.

Artificial Bee Colony Algorithm-based Parameter Estimation of Fractional-order Chaotic System with Time Delay

It is an important issue to estimate parameters of fractional-order chaotic systems in nonlinear science, which has received increasing interest in recent years. In this paper, time delay and fractional order as well as system’s parameters are concerned by treating the time delay and fractional order as additional parameters. The parameter estimation is converted into a multi-dimensional optimization problem. A new scheme based on artificial bee colony(ABC) algorithm is proposed to solve the optimization problem. Numerical experiments are performed on two typical time-delay fractional-order chaotic systems to verify the effectiveness of the proposed method.

Aeroengine Nonlinear Sliding Mode Control Based on Artificial Bee Colony Algorithm

For a class of aeroengine nonlinear systems,a novel nonlinear sliding mode controller(SMC)design method based on artificial bee colony(ABC)algorithm is proposed.In view of the strong nonlinearity and uncertainty of aeroengines,sliding mode control strategy is adopted to design controller for the aeroengine.On basis of exact linearization approach,the nonlinear sliding mode controller is obtained conveniently.By using ABC algorithm,the parameters in the designed controller can be tuned to achieve optimal performance,resulting in a closedloop system with satisfactory dynamic performance and high steady accuracy.Simulation on an aeroengine verifies the effectiveness of the presented method.

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines,and the rationality of their routes plays the direct impact on operation safety and energy consumption.Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil,however,less works are for electric trackless rubber-tyred vehicles.Furthermore,energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving,especially the limited cruising ability of electric trackless rubber-tyred vehichles(TRVs).To address this issue,an energy consumption model of an electric trackless rubber-tyred vehicle is formulated,in which the effects from total mass,speed profiles,slope of roadways,and energy management mode are all considered.Following that,a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance,allowable load,and endurance power.As a problem-solver,an improved artificial bee colony algorithm is put forward.More especially,an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space.In order to assign onlookers to some promising food sources reasonably,their selection probability is adaptively adjusted.For a stagnant food source,a knowledge-driven initialization is developed to generate a feasible substitute.The experimental results on four real-world instances indicate that improved artificial bee colony algorithm(IABC)outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

Indoor evacuation model based on visual-guidance artificial bee colony algorithm

Research on evacuation simulation and modeling is an important and urgent issue for emergency management.This paper presents an evacuation model based on cellular automata and social force to simulate the evacuation dynamics.Attractive force of target position,repulsive forces of individuals and obstacles,as well as congestion are considered in order to simulate the interaction among evacuees and the changing environment.A visual-guidance-based artificial bee colony algorithm is proposed to optimize the evacuation process.Each evacuee moves toward exits with the guidance of leading bee in his/her visual field.And leading bee is selected according to comprehensive factors including distance from the current individual,the number of obstacles and congestion,which avoids the randomness of roulette mechanism used by basic artificial bee colony algorithm.The experimental results indicate that the proposed model and algorithm can achieve effective performances for indoor evacuation problems with a large number of evacuees and obstacles,which accords with the actual evacuation situation.

Application of discrete artificial bee colony algorithm for cloud task optimization scheduling

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system.This study mainly analyzed the application of the artificial bee colony(ABC)algorithm in the cloud task scheduling.In order to solve the problem of cloud task scheduling,the ABC algorithm was discretized to get the discrete artificial bee colony(DABC)algorithm.Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm.Finally,the simulation experiment was carried out,and the performance of first-come-first-served(FCFS),MIN–MIN,ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm.The results showed that the user waiting time of the DABC algorithm was 1210s,the load balance degree was 0.01,and the user payment fee was 1688 yuan when the number of cloud tasks was 500;compared with other algorithms,the user waiting time of the DABC algorithm was shorter,the resource load balance degree was higher,and the overall performance was better.The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling,and it can be further extended and applied in practice.

Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.

A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head.The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network.The proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster head.The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.

Improved Random Forest Algorithm Based on Adaptive Step Size Artificial Bee Colony Optimization

The traditional random forest algorithm works along with unbalanced data,cannot achieve satisfactory prediction results for minority class,and suffers from the parameter selection dilemma.In view of this problem,this paper proposes an unbalanced accuracy weighted random forest algorithm(UAW_RF)based on the adaptive step size artificial bee colony optimization.It combines the ideas of decision tree optimization,sampling selection,and weighted voting to improve the ability of stochastic forest algorithm when dealing with biased data classification.The adaptive step size and the optimal solution were introduced to improve the position updating formula of the artificial bee colony algorithm,and then the parameter combination of the random forest algorithm was iteratively optimized with the advantages of the algorithm.Experimental results show satisfactory accuracies and prove that the method can effectively improve the classification accuracy of the random forest algorithm.

Maintenance Optimization for EMU Trains Considering Environmental Impacts and Correlations

The complexity of the actual operating environment of EMU trains and the interaction between the reliability of system components have become a huge challenge for the maintenance scheduling of EMU trains. In response to these problems, the evolution of reliability and failure rate under the influence of environmental factors, failure correlations and economy correlations is analyzed. We assume bogie systems form the EMU train in series. The failure correlation matrix of the bogie systems is modeled. With the lowest total maintenance cost as the optimization objective, a decision-making model for EMU train maintenance is established. A dynamic maintenance strategy is proposed for the model, which can improve maintenance plans efficiently. Artificial bee colony algorithm is applied to further iteratively optimize the threshold parameters in the strategy. The results are calculated and verified by a numerical example. The results show the effectiveness of the maintenance decision model. The dynamic maintenance strategy in this paper is compared with the traditional opportunistic maintenance strategy. The proposed maintenance strategy outperforms the traditional opportunistic maintenance strategy in the numerical example.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

An improved artificial bee colony-random forest(IABC-RF)model for predicting the tunnel deformation due to an adjacent foundation pit excavation

An improved artificial bee colony-random forest(IABC-RF)model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit.A new search strategy of the artificial bee colony(ABC)algorithm is herein developed and incorporated,with the results showing that a much higher computational efficiency can be achieved with the new model,while high computational accuracy can also be maintained.The improved ABC algorithm is thereafter utilised and combined with the random forest(RF)model,where four important hyper-parameters are optimized,for a tunnel deformation prediction.Results are thoroughly compared with those of other prediction methods based on machine learning(ML),as well as the monitored data on the site.Via the comparisons,the validity and effectiveness of the proposed model are fully demonstrated,and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering.

Identification and nonlinear model predictive control of MIMO Hammerstein system with constraints

This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.