基于免疫算法PID控制器参数的优化设计

根据生物免疫系统的特性,提出一种基于免疫进化计算PID控制器参数的优化设计算法.免疫进化算法引入记忆细胞的抗体浓度调节机制,具有种群的多样性,能确保快速稳定地收敛到全局的最优点.仿真实验表明该算法简便有效.

多目标优化演化算法研究综述

演化计算来源于自然界进化过程的灵感和进化思想的观点。它的潜在并行性及自组织、自适应、自学习的智能特性对于求解多目标优化问题具有巨大的潜力。系统地介绍多目标进化算法(MOEAs),同时讨论进化算法(EAs)在多目标优化应用中的几个关键性问题及今后需进一步研究的工作,并做总结和展望。

AC-PSO ALGORITHM FOR UAV MISSION PLANNING

Choosing the best path during unmanned air vehicle （UAV） flying is the target of the UAV mission planning problem. Because of its nearly constant flight height, the UAV mission planning problem can be treated as a 2-D （horizontal） path arrangement problem. By modeling the antiaircraft threat, the UAV mission planning can be mapped to the traveling seaman problem （TSP）. A new algorithm is presented to solve the TSP. The algorithm combines the traditional ant colony system （ACS） with particle swarm optimization （PSO）, thus being called the AC-PSO algorithm. It uses one by one tour building strategy like ACS to determine that the target point can be chosen like PSO. Experiments show that AC-PSO synthesizes both ACS and PSO and obtains excellent solution of the UAV mission planning with a higher accuracy.

Fast algorithm for constructing neighbor-joining phylogenetic trees

To improve the performance of Saitou and Nei＇s algorithm （SN） and Studier and Keppler＇s improved algorithm （SK） for constructing neighbor-joining phylogenetic trees and reduce the time complexity of the computation, a fast algorithm is proposed. The proposed algorithm includes three techniques. First, a linear array A[N] is introduced to store the sum of every row of the distance matrix （the same as SK）, which can eliminate many repeated computations. Secondly, the value of A [i] is computed only once at the beginning of the algorithm, and is updated by three elements in the iteration. Thirdly, a very compact formula for the sum of all the branch lengths of operational taxonomic units （OTUs） i and j is designed, and the correctness of the formula is proved. The experimental results show that the proposed algorithm is from tens to hundreds times faster than SN and roughly two times faster than SK when N increases, constructing a tree with 2 000 OTUs in 3 min on a current desktop computer. To earn the time with the cost of the space and reduce the computations in the innermost loop are the basic solutions for algorithms with many loops.

Lake Eutrophic Evaluation Based on Bee Immune Evolutionary Algorithm

In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the ＂ normalized values＂ of multi-indexes.The parameters in the formula were also optimized by bee immune evolutionary algorithm（BEIEA）.The universal index formula was suitable to multiindices items for eutrophic evaluation.At the same time,the formula was applied to practical eutrophic evaluations in 10 regions of Dong Lake.The evaluation results were coincident with those obtained from the power function of weighted sums and also with actual conditions.It was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model.

Modified particle swarm optimization-based antenna tilt angle adjusting scheme for LTE coverage optimization

In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.

An Improved Differential Evolution for Optimization of Chemical Process

Differential evolution （DE） is an evolutionary optimization method, which has been successfully used in many practical cases. However, DE involves large computation time, especially, when used to optimize the compurationally expensive objective function. To overcome this .difficulty, the concept of immunity based on vaccination is used to help proliferate excellent schemata and to restrain the degenerate phenomenon. To improve the effective- ness of vaccines, a new vaccine autonomous obtaining method, and a method of deciding the probability of vacci- nation are proposed. In addition, a method for modifying the search space dynamically is proposed to enhance the possibility of converging to the true global optimum. Experiments showed that the improved DE performs better than the classical DE significantly.

Design and optimization in multiphase homing trajectory of parafoil system

In order to realize safe and accurate homing of parafoil system,a multiphase homing trajectory planning scheme is proposed according to the maneuverability and basic flight characteristics of the vehicle.In this scenario,on the basis of geometric relationship of each phase trajectory,the problem of trajectory planning is transformed to parameter optimizing,and then auxiliary population-based quantum differential evolution algorithm(AP-QDEA)is applied as a tool to optimize the objective function,and the design parameters of the whole homing trajectory are obtained.The proposed AP-QDEA combines the strengths of differential evolution algorithm(DEA)and quantum evolution algorithm(QEA),and the notion of auxiliary population is introduced into the proposed algorithm to improve the searching precision and speed.The simulation results show that the proposed AP-QDEA is proven its superior in both effectiveness and efficiency by solving a set of benchmark problems,and the multiphase homing scheme can fulfill the requirement of fixed-points and upwind landing in the process of homing which is simple in control and facile in practice as well.

Differential evolution with controlled search direction

A novel and simple technique to control the search direction of the differential mutation was proposed.In order to verify the performance of this method,ten widely used benchmark functions were chosen and the results were compared with the original differential evolution(DE)algorithm.Experimental results indicate that the search direction controlled DE algorithm obtains better results than the original DE algorithm in term of the solution quality and convergence rate.

Improvements in seismic event locations in a deep western U.S. coal mine using tomographic velocity models and an evolutionary search algorithm

Methods of improving seismic event locations were investigated as part of a research study aimed at reducing ground control safety hazards. Seismic event waveforms collected with a 23-station three-dimensional sensor array during longwall coal mining provide the data set used in the analyses. A spatially variable seismic velocity model is constructed using seismic event sources in a passive tomographic method. The resulting three-dimensional velocity model is used to relocate seismic event positions. An evolutionary optimization algorithm is implemented and used in both the velocity model development and in seeking improved event location solutions. Results obtained using the different velocity models are compared. The combination of the tomographic velocity model development and evolutionary search algorithm provides improvement to the event locations.

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.

Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms

Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.

Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes

Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.

Ship Hull Form Optimization by Evolutionary Algorithm in Order to Diminish the Drag

This study presents a numerical method for optimizing hull form in calm water with respect to total drag which contains a viscous drag and a wave drag. The ITTC 1957 model-ship correlation line was used to predict frictional drag and the corrected linearized thin-ship theory was employed to estimate the wave drag The evolution strategy （ES） which is a member of the evolutionary algorithms （EAs） family obtains an optimum hull form by considering some design constraints. Standard Wigley hull is considered as an initial hull in optimization procedures for two test cases and new hull forms were achieved at Froude numbers 0.24, 0.316 and 0.408. In one case the ES technique was ran for the initial hull form, where the main dimensions were fixed and the only variables were the hull offsets. In the other case in addition to hull offsets, the raain dimensions were considered as variables that are optimized simultaneously. The numerical results of optimization procedure demonstrate that the optimized hull forms yield a reduction in total drag.

Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemical process in industry [ 1 ]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta- neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob- lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm （SADE） to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm （ISADE）. Optimization results indicate that application oflSADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

An evolutionary particle filter based EM algorithm and its application

In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. This step replaces the resampling stage needed by most particle filters and relieves the effect caused by sample impoverishment. A nonlinear tracking problem shows that this new approach outperforms other related particle filters.

The Properties Analysis for Generalized Abstract Evolutionary Algorithm

There has been a growing interest in mathematical models to character the evolutionary algorithms. The best-known one of such models is the axiomatic model called the abstract evolutionary algorithm （AEA）, which unifies most of the currently known evolutionary algorithms and describes the evolution as an abstract stochastic process composed of two fundamental abstract operators： abstract selection and evolution operators. In this paper, we first introduce the definitions of the generalized abstract selection and evolution operators. Then we discuss the characterization of some parameters related to generalized abstract selection and evolution operators. Based on these operators, we finally give the strong convergence of the generalized abstract evolutionary algorithm. The present work provides a big step toward the establishment of a unified theory of evolutionary computation.

Immunity clone algorithm with particle swarm evolution

Combining the clonal selection mechanism of the immune system with the evolution equations of particle swarm optimization, an advanced algorithm was introduced for functions optimization. The advantages of this algorithm lies in two aspects. Via immunity operation, the diversity of the antibodies was maintained, and the speed of convergent was improved by using particle swarm evolution equations. Simulation programme and three functions were used to check the effect of the algorithm. The advanced algorithm were compared with clonal selection algorithm and particle swarm algorithm. The results show that this advanced algorithm can converge to the global optimum at a great rate in a given range, the performance of optimization is improved effectively.

The Convergence of the Abstract Evolutionary Algorithm Based on a Special Selection Mechanism

There has been a growing interest in mathematical models to character the evolutionary algorithms. The best-known one of such models is the axiomatic model colled the abstract evolutionary algorithm. In this paper, we first introduce the definitions of the abhstract selection and evolution operators, and that of the abstract evolutionary algorithm, which describes the evolution as an abstract stochastic process composed of these two fundamental abstract operators. In particular, a kind of abstract evolutionary algorithms based on a special selection mechansim is discussed. According to the sorting for the state space, the properties of the single step transition matrix for the algorithm are anaylzed. In the end, we prove that the limit probability distribution of the Markov chains exists. The present work provides a big step toward the establishment of a unified theory of evolutionary computation.