Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Unfolding neutron spectra from water-pumping-injection multilayered concentric sphere neutron spectrometer using self-adaptive differential evolution algorithm

A self-adaptive differential evolution neutron spectrum unfolding algorithm(SDENUA)is established in this study to unfold the neutron spectra obtained from a water-pumping-injection multilayered concentric sphere neutron spectrometer(WMNS).Specifically,the neutron fluence bounds are estimated to accelerate the algorithm convergence,and the minimum error between the optimal solution and input neutron counts with relative uncertainties is limited to 10^(-6)to avoid unnecessary calculations.Furthermore,the crossover probability and scaling factor are self-adaptively controlled.FLUKA Monte Carlo is used to simulate the readings of the WMNS under(1)a spectrum of Cf-252 and(2)its spectrum after being moderated,(3)a spectrum used for boron neutron capture therapy,and(4)a reactor spectrum.Subsequently,the measured neutron counts are unfolded using the SDENUA.The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA,which does not require complex parameter tuning or an a priori default spectrum.The results indicate that the solutions of the SDENUA agree better with the IAEA spectra than those of MAXED and GRAVEL in UMG 3.1,and the errors of the final results calculated using the SDENUA are less than 12%.The established SDENUA can be used to unfold spectra from the WMNS.

A self-adaptive linear evolutionary algorithm for solving constrained optimization problems

In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce a novel strategy for evaluating individual＇s relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ＇good＇ individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.

Enhanced self-adaptive evolutionary algorithm for numerical optimization

There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.

Dynamic self-adaptive ANP algorithm and its application to electric field simulation of aluminum reduction cell

Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.

Generalized Self-Adaptive Genetic Algorithms

In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.

Differential Evolution Algorithm Based Self-adaptive Control Strategy for Fed-batch Cultivation of Yeast

In the fed-batch cultivation of Saccharomyces cerevisiae,excessive glucose addition leads to increased ethanol accumulation,which will reduce the efficiency of glucose utilization and inhibit product synthesis.Insufficient glucose addition limits cell growth.To properly regulate glucose feed,a different evolution algorithm based on self-adaptive control strategy was proposed,consisting of three modules(PID,system identification and parameter optimization).Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental cultivations.In the simulation,cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration,more stable ethanol concentration around the set-point(1.0 g·L^(-1)),and final biomass concentration of 34.5 g-DCW·L^(-1),29.2%higher than that with a conventional PID control strategy.In the experiment,the cultivation with the self-adaptive control strategy also had more stable glucose and ethanol concentrations,as well as a final biomass concentration that was 37.4%higher than that using the conventional strategy.

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm

Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

Self-Adaptive Algorithms for the Split Common Fixed Point Problem of the Demimetric Mappings

The split common fixed point problem is an inverse problem that consists in finding an element in a fixed point set such that its image under a bounded linear operator belongs to another fixed-point set. In this paper, we present new iterative algorithms for solving the split common fixed point problem of demimetric mappings in Hilbert spaces. Moreover, our algorithm does not need any prior information of the operator norm. Weak and strong convergence theorems are given under some mild assumptions. The results in this paper are the extension and improvement of the recent results in the literature.

A Gradient-Simulated Annealing Algorithm of Pre-location-Based Best Fitting of Blank to Complex Surfaces Machining

The algorithm is divided into two steps. The first step pre-locates the blank by aligning its centre of gravity and approximate normal vector with those of destination surfaces, with largest overlap of projections of two objects on a plane perpendicular to the normal vector. The second step is optimizing an objective function by means of gradient-simulated annealing algorithm to get the best matching of a set of distributed points on the blank and destination surfaces. An example for machining hydroelectric turbine blades is given to verify the effectiveness of algorithm.

基于Vector Fitting的光伏并网逆变器控制器参数频域辨识方法

光伏并网逆变器通常含有内外环、锁相环等不同带宽控制环节,且控制器参数往往并不可知,即存在“灰箱”问题。为准确辨识不同带宽控制器参数,提出一种基于端口导纳特性的光伏并网逆变器控制器参数频域辨识方法。首先,建立典型控制下光伏并网逆变器交流端口的dq理论导纳模型,得到其理论导纳标准式;然后,通过扫频手段获得光伏并网逆变器交流端口的测量导纳数据,并采用Vector Fitting算法对测量的端口导纳数据进行矢量拟合,得到拟合导纳标准式;最后,运用最小二乘原理使理论导纳标准式与拟合导纳标准式对应项系数差值的平方和最小,从而辨识得到光伏并网逆变器控制器参数的估计值。参数辨识实例表明,所提方法能够同时准确辨识出不同带宽控制器参数。

No Fit Polygon for Nesting Problem Solving with Hybridizing Ant Algorithms

In design science, these two kinds of problems are mutually nested, however, the nesting could not blind us for the fact that their problem-solving and solution justification methods are different. The ant algorithms research field, builds on the idea that the study of the behavior of ant colonies or other social insects is interesting, because it provides models of distributed organization which could be utilized as a source of inspiration for the design of optimization and distributed control algorithms. In this paper, a relatively new type of hybridizing ant search algorithm is developed, and the results are compared against other algorithms. The intelligence of this heuristic approach is not portrayed by individual ants, but rather is expressed by the colony as a whole inspired by labor division and brood sorting. This solution obtained by this method will be evaluated against the one obtained by other traditional heuristics.

Iterative circle fitting based on circular attracting factor

An intuitive method for circle fitting is proposed. Assuming an approximate circle(CA,n) for the fitting of some scattered points, it can be imagined that every point would apply a force to CA,n, which all together form an overall effect that "draws" CA,n towards best fitting to the group of points. The basic element of the force is called circular attracting factor(CAF) which is defined as a real scalar in a radial direction of CA,n. An iterative algorithm based on this idea is proposed, and the convergence and accuracy are analyzed. The algorithm converges uniformly which is proved by the analysis of Lyapunov function, and the accuracy of the algorithm is in accord with that of geometric least squares of circle fitting. The algorithm is adopted to circle detection in grayscale images, in which the transferring to binary images is not required, and thus the algorithm is less sensitive to lightening and background noise. The main point for the adaption is the calculation of CAF which is extended in radial directions of CA,n for the whole image. All pixels would apply forces to CA,n, and the overall effect of forces would be equivalent to a force from the centroid of pixels to CA,n. The forces from would-be edge pixels would overweigh that from noisy pixels, so the following approximate circle would be of better fitting. To reduce the amount of calculation, pixels are only used in an annular area including the boundary of CA,n just in between for the calculation of CAF. Examples are given, showing the process of circle fitting of scattered points around a circle from an initial assuming circle, comparing the fitting results for scattered points from some related literature, applying the method proposed for circular edge detection in grayscale images with noise, and/or with only partial arc of a circle, and for circle detection in BGA inspection.

GENETIC ALGORITHM WITH FUZZY FITNESS EVALUATION

Using a fuzzy estimator to evaluate the fitness of chromosomes in a genetic algorithm and adaptively training it in the evolutionary process, the genetic algorithm with fuzzy fitness evaluation is proposed to reduce the computation time of the algorithm. An analysis on the optimization performance of the proposed algorithm shows that it maintains good performance with its computation time saved. Finally, simulation results on design of a fuzzy controller are presented.

A reordered first fit algorithm based novel storage scheme for parallel turbo decoder

In this paper we discuss a novel storage scheme for simultaneous memory access in parallel turbo decoder. The new scheme employs vertex coloring in graph theory. Compared to a similar method that also uses unnatural order in storage, our scheme requires 25 more memory blocks but allows a simpler configuration for variable sizes of code lengths that can be implemented on-chip. Experiment shows that for a moderate to high decoding throughput （40-100 Mbps）, the hardware cost is still affordable for 3GPP＇s （3rd generation partnership project） interleaver.

A self-adaptive stochastic resonance system design and study in chaotic interference

The us of stochastic resonance （SR） can effectively achieve the detection of weak signal in white noise and colored noise. However, SR in chaotic interference is seldom involved. In view of the requirements for the detection of weak signal in the actual project and the relationship between the signal, chaotic interference, and nonlinear system in the bistable system, a self-adaptive SR system based on genetic algorithm is designed in this paper. It regards the output signal-to-noise ratio （SNR） as a fitness function and the system parameters are jointly encoded to gain optimal bistable system parameters, then the input signal is processed in the SR system with the optimal system parameters. Experimental results show that the system can keep the best state of SR under the condition of low input SNR, which ensures the effective detection and process of weak signal in low input SNR.

Multi-path planning algorithm based on fitness sharing and species evolution

A new algorithm is proposed for underwater vehicles multi-path planning. This algorithm is based on fitness sharing genetic algorithm, clustering and evolution of multiple populations, which can keep the diversity of the solution path, and decrease the operating time because of the independent evolution of each subpopulation. The multi-path planning algorithm is demonstrated by a number of two-dimensional path planning problems. The results show that the multi-path planning algorithm has the following characteristics: high searching capability, rapid convergence and high reliability.

Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections.Both of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability.Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs.But one such difficulty was reducing the cluster number under increasing transmitting nodes.This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for VANET.The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles.In addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given space.The DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean distance.The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects.The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications.In spite of this,it is challenging to design energy-efficient WSN.The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network.In order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network development.In this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for WSN.The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN.To accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution quality.Besides,the inclusion of DE in traditional AOA helps in overcoming local optima problems.In addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance.In order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.