Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions

A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.

Hybrid anti-prematuration optimization algorithm

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.

Structural Dynamic Optimization for Flexible Beam of Helicopter Rotor Based on GA

As one of the most important steps in the design of bearing-less rotor systems,the design of flexible beam has received much research attention.Because of the very complex working environment of helicopter,the flexible beam should satisfy both the strength and dynamic requirements.However,traditional optimization research focused only on either the strength or dynamical characteristics.To sufficiently improve the performance of the flexible beam,both aspects must be considered.This paper proposes a two-stage optimization method based on the Hamilton variational principle:Variational asymptotic beam section analysis(VABS)program and genetic algorithm(GA).Consequently,a two-part analysis model based on the Hamilton variational principle and VABS is established to calculate section characteristics and structural dynamics characteristics,respectively.Subsequently,the two parts are combined to establish a two-stage optimization process and search with GA to obtain the best dynamic characteristics combinations.Based on the primary optimization results,the section characteristics of the flexible beam are further optimized using GA.The optimization results show that the torsional stiffness decreases by 36.1%compared with the full 0°laying scheme without optimization and the dynamic requirements are achieved.The natural frequencies of flapping and torsion meet the requirements(0.5 away from the passing frequencies of the blade,0.25 away from the excitation force frequency,and the flapping and torsion frequencies keep a corresponding distance).The results indicate that the optimization method can significantly improve the performance of the flexible beam.

Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines

Support vector machine (SVM) is a popular pattern classification method with many application areas. SVM shows its outstanding performance in high-dimensional data classification. In the process of classification, SVM kernel parameter setting during the SVM training procedure, along with the feature selection significantly influences the classification accuracy. This paper proposes two novel intelligent optimization methods, which simultaneously determines the parameter values while discovering a subset of features to increase SVM classification accuracy. The study focuses on two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine above the two intelligent optimization methods with SVM to choose appropriate subset features and SVM parameters, which are termed GA-FSSVM (Genetic Algorithm-Feature Selection Support Vector Machines) and PSO-FSSVM(Particle Swarm Optimization-Feature Selection Support Vector Machines) models. Experimental results demonstrate that the classification accuracy by our proposed methods outperforms traditional grid search approach and many other approaches. Moreover, the result indicates that PSO-FSSVM can obtain higher classification accuracy than GA-FSSVM classification for hyperspectral data.

A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features.Evolutionary computing(EC)is promising for FS owing to its powerful search capability.However,in traditional EC-based methods,feature subsets are represented via a length-fixed individual encoding.It is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training time.This work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional FS.In LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively.Moreover,a dominance-based local search method is employed for further improvement.The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

Optimized selection of suitable sites for farmland consolidation projects using multi-objective genetic algorithms

In order to select suitable sites for farmland consolidation projects,correlation analysis and evolutionary algorithms were used to optimize the evaluation of ecological,social and economic factors,avoiding subjective selection and ignorance of spatial relationships among land attributes.Multi-objective Genetic Algorithms(MOGA)were applied to select the best sites from the perspective of spatial relationship and land attribute evaluation.With carefully defined restrictions and variables,multi-objective optimization is able to select several suitable sites for farmland consolidation projects.The results from a case study in Yangshan,Guangdong of China showed that the selected sites were on the central and southern Yangshan with expected flat terrain and abundant water resources.An empirical experiment also demonstrated that the proposed method is able to provide well selected sites for land consolidation projects.

Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV

To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible.

Genetic algorithm for pareto optimum-based route selection

A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.

An Improved Immune Genetic Algorithm for Solving the Optimization Problems of Computer Communication Networks

Obtaining the average delay and selecting a route in a communication networkare multi-constrained nonlinear optimization problems . In this paper, based on the immune geneticalgorithm, a new fuzzy self-adaptive mutation operator and a new upside-down code operator areproposed. This improved IGA is further successfully applied to solve optimal problems of computercommunication nets.

基于改进遗传算法的PC构件生产调度优化

基于流水线车间生产调度理论,建立以生产成本为决策变量、以最大化生产利润为目标的数学模型;结合预制构件车间生产的实际情况,设计最优选择法、局部变异算子改进遗传算法,求解最优生产调度方案。某预制构件公司订单生产调度仿真研究表明,基于改进遗传算法的PC构件生产调度优化可以在较短的时间内获得更高的利润,为提高预制构件智能化生产效率提供了技术基础。

求解带容量约束车辆路径问题的改进遗传算法

为解决传统遗传算法求解带容量约束的车辆路径问题时收敛速度慢和局部搜索能力差的问题,对传统遗传算法提出一种改进策略。使用基于贪婪策略的启发式交叉算子加强算法接近最优解的能力,加快算法收敛速度,在变异操作中,引入最近邻搜索算子,缩小基因变异范围,使用单点局部插入算子提高算法的局部优化能力。采用精英选择和轮盘赌法结合的选择策略,保持种群多样性以加强算法的全局搜索能力。实例计算测试表明,与传统遗传算法相比,所提算法求解平均偏差降低了70.25%,求解时间减少了87.41%;与ALNS和AGGWOA算法相比,有更高的求解质量和更好的稳定性。

基于改进实数遗传算法的桑叶采摘机结构参数优化

针对遗传算法在求解桑叶采摘机结构优化问题时容易陷入局部最优和求解精度低等问题,提出一种改进实数遗传算法。首先是给出一种基于序的组合适应度函数的轮盘赌选择算子,该算子在轮盘赌的基础上,通过一个自适应变化的参数在两种适应度函数中选择一个,再去计算适应度值;然后设计一种基于方向的改进启发式交叉算子,该算子既保留两个父代个体中较优个体对子代个体的影响,又增加种群中最优个体对子代个体的影响,提高交叉产生有潜力子代的可能性。接着将改进算法应用于摇杆式桑叶采摘机的优化参数设计中,通过与其他算法作仿真对比试验验证算法的优越性,获得采摘机最优参数组合:行走结构速度为24 mm/s、拨动结构角速度为1.2 rad/s、采摘结构速度为440 mm/s,并由运行结果可知整机性能与优化前相比提高13%。最后用优化得到的参数组合进行实地试验,结果显示桑叶采摘机性能提升10.9%,误差较小为2.1%。可见,所提改进实数遗传算法是优化采摘机参数的一种有效算法。

基于组合变异和分组优化的单亲遗传算法求解旅行商问题

针对遗传算法求解旅行商问题存在收敛速度慢、容易陷入局部最优等问题,提出了基于组合变异和分组优化的单亲遗传算法。算法设计了由双侧倒序、近邻交换、跳跃基因构成的组合变异算子,用于扩大搜索范围,增强种群的多样性;经过精英优选后,将种群按适应度优劣分为两组作局部优化,对优质互异组依次采用插入和2opt算子,加快进化收敛速度;对普通组用倒序算子,增强其跳出局部最优的能力。仿真实验表明,对于中小型规模的旅行商问题,该算法在收敛速度和求解能力上得到明显改善和增强。

Optimization of maintenance strategy for high-speed railwaycatenary system based on multistate model

A multi-objective optimization model considering both reliability and maintenance cost is proposed to solve the contradiction between reliability and maintenance cost in high-speed railway catenary system maintenance activities.The non-dominated sorting genetic algorithm 2(NSGA2)is applied to multi-objective optimization,and the optimization result is a set of Pareto solutions.Firstly,multistate failure mode analysis is conducted for the main devices leading to the failure of catenary,and then the reliability and failure mode of the whole catenary system is analyzed.The mathematical relationship between system reliability and maintenance cost is derived considering the existing catenary preventive maintenance mode to improve the reliability of the system.Secondly,an improved NSGA2(INSGA2)is proposed,which strengths population diversity by improving selection operator,and introduces local search strategy to ensure that population distribution is more uniform.The comparison results of the two algorithms before and after improvement on the zero-ductility transition(ZDT)series functions show that the population diversity is better and the solution is more uniform using INSGA2.Finally,the INSGA2 is applied to multi-objective optimization of system reliability and maintenance cost in different maintenance periods.The decision-makers can choose the reasonable solutions as the maintenance plans in the optimization results by weighing the relationship between the system reliability and the maintenance cost.The selected maintenance plans can ensure the lowest maintenance cost while the system reliability is as high as possible.

Hybrid Multipopulation Cellular Genetic Algorithm and Its Performance

The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is proposed,which combines population segmentation with particle swarm optimization(PSO).The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.

A genetic algorithm for the pareto optimal solution set of multi-objective shortest path problem

Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem （ MSPP ） has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo- rithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa- per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.

智慧交通场景下云边端协同的多目标优化卸载决策

随着智慧交通、云计算网络以及边缘计算网络的快速发展,车载终端与路基单元、中心云服务器之间的信息交互变得越发频繁。针对智慧交通云边端协同计算场景下如何高效地实现车路云一体化融合感知、群体决策以及各级服务器间对资源的合理分配问题,设计了基于云边端与智慧交通全面融合的网络架构。在该架构下,通过对任务类型的合理划分,再由各服务器对其进行选择性的缓存、卸载;在智慧交通云边端协同计算场景下,依次设计了一种对任务自适应的缓存模型、任务卸载时延模型、系统能量损耗模型、车载用户对服务质量不满意度评价模型、多目标优化问题模型,并给出了一种基于改进型非支配遗传算法的任务卸载决策方案。实验结果表明,文中方案能够有效降低任务卸载过程中所带来的时延和能耗,提高了系统资源利用率,给车辆用户带来更好的服务体验。

循环工质对LNG冷能发电系统性能的影响

朗肯循环可将液化天然气(LNG)冷量[火用]转化为电能,是一种有效的冷能回收利用技术。结合工程实际,将LNG冷能发电系统内最低压力设置为高于常压,探究了循环工质对系统性能的影响。将7种常用工质作为备选,以系统净输出功最大为目标函数,在相同工况下对单级联合循环(CC)、串联联合循环(CCC)和并联联合循环(PCC)3种常见的LNG冷能发电系统进行了优化。分析了选择不同工质时系统性能的差异及其原因,探究了有无直接膨胀过程对工质选择的影响。结果表明,CC、CCC和PCC系统的最优工质分别为CH_(2)F_(2)、C_(2)H_(6)+C_(3)H_(8)和C_(2)H_(4)+C_(3)H_(8)。选择最优工质情况下,CCC系统通过对LNG冷能的有效分段利用,减少了系统内的不可逆[火用]损失,所以性能相对最优,其热效率相比CC和PCC系统分别提升了54.8%和35.4%,且所需换热面积并未显著增加。有无直接膨胀不影响系统内工质选择,但无直接膨胀会导致最大系统净输出功略有降低。