PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

Application of GA, PSO, and ACO Algorithms to Path Planning of Autonomous Underwater Vehicles

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Optimization of Process Parameters of Continuous Microwave Drying Raspberry Puree Based on RSM and ANN-GA

To improve drying uniformity and anthocyanin content of the raspberry puree dried in a continuous microwave dryer,the effects of process parameters(microwave intensity,air velocity,and drying time)on evaluation indexes(average temperature,average moisture content,average retention rate of the total anthocyanin content,temperature contrast value,and moisture dispersion value)were investigated via the response surface method(RSM)and the artificial neural network(ANN)with genetic algorithm(GA).The results showed that the microwave intensity and drying time dominated the changes of evaluation indexes.Overall,the ANN model was superior to the RSM model with better estimation ability,and higher drying uniformity and anthocyanin retention rate were achieved for the ANN-GA model compared with RSM.The optimal parameters were microwave intensity of 5.53 W•g^(-1),air velocity of 1.22 m·s^(-1),and drying time of 5.85 min.This study might provide guidance for process optimization of microwave drying berry fruits.

New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to 7G.Furthermore,it improves the array gain and directivity,increasing the detection range and angular resolution of radar systems.This study proposes two highly efficient SLL reduction techniques.These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,respectively.The convolution process determines the element’s excitations while the GA optimizes the element spacing.For M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,respectively.When the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher SLL.Tomitigate the increased HPBWof the odd and even excitations,the element spacing is optimized using the GA.Thereby,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the SLL.Furthermore,for extreme SLL reduction,the DConv/GA is introduced.In this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors.It provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

RESEARCH ON THE MINIMUM ZONE CYLINDRICITY EVALUATION BASED ON GENETIC ALGORITHMS

A genetic algorithm (GA)-based method is proposed to solve the nonlinearoptimization problem of minimum zone cylindricity evaluation. First, the background of the problemis introduced. Then the mathematical model and the fitness function are derived from themathematical definition of dimensioning and tolerancing principles. Thirdly with the least squaressolution as the initial values, the whole implementation process of the algorithm is realized inwhich some key techniques, for example, variables representing, population initializing and suchbasic operations as selection, crossover and mutation, are discussed in detail. Finally, examplesare quoted to verify the proposed algorithm. The computation results indicate that the GA-basedoptimization method performs well on cylindricity evaluation. The outstanding advantages concludehigh accuracy, high efficiency and capabilities of solving complicated nonlinear and large spaceproblems.

Job shop scheduling problem with alternative machines using genetic algorithms

The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.

基于ICGA的星载天线自适应调零算法

有效地抑制干扰,最大程度地保障正常通信,是卫星通信的一项关键技术。 采用基于整数编码遗传算法(IntegerCodedGeneticAlgorithmICGA)的卫星天线 自适应调零算法,利用遗传算法不受初始值选择的局限,全局搜索及整数编码的快速 性,可以实时实现天线调零。计算机仿真实验表明了该算法的有效性和鲁棒性。

FPGA PLACEMENT OPTIMIZATION BY TWO-STEP UNIFIED GENETIC ALGORITHM AND SIMULATED ANNEALING ALGORITHM

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.

New Optimization Method, the Algorithms of Changes, for Heat Exchanger Design

Heat exchangers are widely used in the process engineering such as the chemical industries, the petroleum industries, and the HVAC applications etc. An optimally designed heat exchanger cannot only help the optimization of the equipment size but also the reduction of the power consumption. In this paper, a new optimization approach called algorithms of changes (AOC) is proposed for design and optimization of the shell-tube heat exchanger. This new optimization technique is developed based on the concept of the book of changes (I Ching) which is one of the oldest Chinese classic texts. In AOC, the hexagram operations in I Ching are generalized to binary string case and an iterative process, which imitates the I Ching inference, is defined. Before applying the AOC to the heat exchanger design problem, the new optimization method is examined by the benchmark optimization problems such as the global optimization test functions and the travelling salesman problem (TSP). Based on the TSP results, the AOC is shown to be superior to the genetic algorithms (GA). The AOC is then used in the optimal design of heat exchanger. The shell inside diameter, tube outside diameter, and baffles spacing are treated as the design (or optimized) variables. The cost of the heat exchanger is arranged as the objective function. For the heat exchanger design problem, the results show that the AOC is comparable to the GA method. Both methods can find the optimal solution in a short period of time.

Optimal Structural Design of the Midship of a VLCC Based on the Strategy Integrating SVM and GA

In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.

GA and PSO culled hybrid technique for economic dispatch problem with prohibited operating zones

This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.

GA-based approach to phase compensation of large phased array antennas

The investigation of the effect of electrical and mechanical errors on the performance of a large active phased array antenna is studied. These errors can decrease the antenna performance, for instance, the gain reduction, side lobe level enhancement, and incorrect beam direction. In order to improve the performance of the antenna in the presence of these errors, phase error correction of large phased array antennas using the genetic algorithm（GA） is implemented. By using the phase compensation method, the antenna overall radiation pattern is recovered close to the ideal radiation pattern without error. By applying the simulation data to a 32×40 array of elements with a square grid at the frequency of S-band and measurement of the radiation pattern, the effectiveness of the proposed method is verified.

Pattern synthesis optimization of 3-D ODAR based on improved GA using LSFE method

Pattern synthesis in 3-D opportunistic digital array radar(ODAR) becomes complex when a multitude of antennas are considered to be randomly distributed in a three dimensional space.In order to obtain an optimal pattern,several freedoms must be constrained.A new pattern synthesis approach based on the improved genetic algorithm(GA) using the least square fitness estimation(LSFE) method is proposed.Parameters optimized by this method include antenna locations,stimulus states and phase weights.The new algorithm demonstrates that the fitness variation tendency of GA can be effectively predicted after several "eras" by the LSFE method.It is shown that by comparing the variation of LSFE curve slope,the GA operator can be adaptively modified to avoid premature convergence of the algorithm.The validity of the algorithm is verified using computer implementation.

Deformation prediction model of surrounding rock based on GA-LSSVM-markov

Command protection engineering is the important component of national protection engineering system. To raise the level of its construction, a deformation prediction model is given based on Genetic Algorithm (GA), Least Square Support Vector Machines (LSSVM) and markov theory. Genetic algorithm is used to improve the parameter of LSSVM. Markov predict method is used to improve the precision of the prediction model. Finally, be used to a certain command protection engineering, the accuracy of the algorithm is improved obviously. The model is proved to be credible and precise.

COMBINING FEATURE SCALING ESTIMATION WITH SVM CLASSIFIER DESIGN USING GA APPROACH

This letter adopts a GA (Genetic Algorithm) approach to assist in learning scaling of features that are most favorable to SVM (Support Vector Machines) classifier, which is named as GA-SVM. The relevant coefficients of various features to the classification task, measured by real-valued scaling, are estimated efficiently by using GA. And GA exploits heavy-bias operator to promote sparsity in the scaling of features. There are many potential benefits of this method:Feature selection is performed by eliminating irrelevant features whose scaling is zero, an SVM classifier that has enhanced generalization ability can be learned simultaneously. Experimental comparisons using original SVM and GA-SVM demonstrate both economical feature selection and excellent classification accuracy on junk e-mail recognition problem and Internet ad recognition problem. The experimental results show that comparing with original SVM classifier, the number of support vector decreases significantly and better classification results are achieved based on GA-SVM. It also demonstrates that GA can provide a simple, general, and powerful framework for tuning parameters in optimal problem, which directly improves the recognition performance and recognition rate of SVM.

BP neural network classification on passenger vehicle type based on GA of feature selection

This paper has concluded six features that belong to passenger vehicle types based on genetic algorithm(GA)of feature selection.We have obtained an optimal feature subset,including length,ratio of width and length,and ratio of height and length.And then we apply this optimal feature subset as well as another feature set,containing length,width and height,to the network input.Back-propagation(BP)neural network and support vector machine(SVM)are applied to classify the passenger vehicle type.There are four passenger vehicle types.This paper selects 400 samples of passenger vehicles,among which 320 samples are used as training set(each class has 80 samples)and the other 80 samples as testing set,taking the feature of the samples as network input and taking four passenger vehicle types as output.For the test,we have applied BP neural network to choose the optimal feature subset as network input,and the results show that the total classification accuracy rate can reach 96%,and the classification accuracy rate of first type can reach 100%.In this condition,we obtain a conclusion that this algorithm is better than the traditional ones[9].

Optimization of total harmonic current distortion and torque pulsation reduction in high-power induction motors using genetic algorithms

This paper presents a powerful application of genetic algorithm （GA） for the minimization of the total harmonic current distortion （THCD） in high-power induction motors fed by voltage source inverters, based on an approximate harmonic model. That is, having defined a desired fundamental output voltage, optimal pulse patterns （switching angles） are determined to produce the fundamental output voltage while minimizing the THCD. The complete results for the two cases of three and five switching instants in the first quarter period of pulse width modulation （PWM） waveform are presented. Presence of harmonics in the stator excitation leads to a pulsing-torque component. Considering the fact that if the pulsing-torques are at low frequencies, they can cause troublesome speed fluctuations, shaft fatigue, and unsatisfactory performance in the feedback control system, the 5th, 7th, 1 lth, and 13th current harmonics （in the case of five switching angles） are constrained at some pre-specified values, to mitigate the detrimental effects of low-frequency harmonics. At the same time, the THCD is optimized while the required fundamental output voltage is maintained.

Performance Prediction of Carbon Fiber Protofilament Based on SAGA-SVR

The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately.

MATHEMATICAL ANALYSIS OF MUTATION OPERATOR AND ITS IMPROVED STRATEGY IN GENETIC ALGORITHMS

This paper analyzes the optimization problem of mutation probability in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mutation probability in different conditions. The superior convergence property of the new method is evaluated by applying it to two simulation examples.

Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms

Many factors influencing range of extended range guided munition （ERGM） are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm （GA） is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem.