Parameterisation and efficient MCMC estimation of non-Gaussian state space models

The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MCMC) algorithms for two non-Gaussian state space models is examined. Specifically, focus is given to particular forms of the stochastic conditional duration (SCD) model and the stochastic volatility (SV) model, with four alternative parameterisations of each model considered. A controlled experiment using simulated data reveals that relationships exist between the simulation efficiency of the MCMC sampler, the magnitudes of the population parameters and the particular parameterisation of the state space model. Results of an empirical analysis of two separate transaction data sets for the SCD model, as well as equity and exchange rate data sets for the SV model, are also reported. Both the simulation and empirical results reveal that substantial gains in simulation efficiency can be obtained from simple reparameterisations of both types of non-Gaussian state space models.     

混合文化优化算法及在车间调度中的应用

针对文化算法收敛速度慢、易陷入局部最优解以及种群多样性少的问题, 本文对文化算法进行优化设计, 提出一种将带有精英保留策略的遗传算法(GA)和模拟退火算法(SA)纳入文化算法(CA)框架的混合优化算法. 此算法基于协同进化的思想, 算法分为下层种群空间和上层信念空间, 两个空间采用了相同的进化机制, 但使用不同的参数. 在文化算法的基础上加入带有精英保留策略的遗传算法, 使种群中的优秀个体直接进入下一代, 以此提高收敛速度; 加入模拟退火算法, 利用其具有突变的特点, 概率性的跳出局部最优并接受劣质解, 以此增加种群多样性. 函数优化结果证明了算法的有效性, 将此算法用于求解最小化最大完工时间的流水车间调度问题, 仿真结果显示, 此算法在收敛速度和精度方面都优于其他几个具有代表性的算法.     

A cellular automaton simulation tool for modelling seismicity in the region of Xanthi

Seismicity is an extended geophysical characteristic of the Greek dominion. There are certain areas of high seismic activity, as well as, regions of low seismicity where strong earthquakes are rather rare events. Consequently, it is of great interest to present a methodology concerning the earthquake process in Greece even for areas considered to be of low seismicity. In this paper, a study of the earthquake activity of an area in Northeastern Greece, centred at Xanthi, Thrace, extending over a region of radius R = 80 km, during a certain time period is presented. A two-dimensional cellular automaton (CA) dynamic system consisting of cells representing charges is used for the simulation of the earthquake process. The model has been tested as well as calibrated using the recorded events of the above-mentioned region as initial conditions. The simulation results are found in good quantitative and qualitative agreement with the Gutenberg–Richter (GR) scaling relations. Finally, the CA model has a user-friendly interface and enables the user to change several of its parameters, in order to study various hypotheses concerning the seismicity of the region under consideration.     

基于遗传算法的飞机定检离位工作流程优化

首次将遗传算法(GA)应用于飞机定检离位工作流程优化中。本文借鉴关键路线法思想建立离位工作流程多约束优化模型,根据可行解变换法思想设计编码和解码方法,并采用经过模拟退火算子和精英选择算子改进后的GA求解。仿真结果表明,在解决多约束优化问题上,改进遗传算法的最优解搜索能力较基本遗传算法有明显提高;优化后离位工作完成时间较优化前缩短14.70%,验证GA在解决定检离位工作流程优化问题上的适用性。     

Correlation-based fault-tolerant controller design for MIMO systems under the Youla parameterisation

This paper develops a correlation-based method into the Youla parameterisation structure for a fault-tolerant controller design strategy. By tuning the Youla parameters with the proposed correlation-based algorithm, a number of conditional faults described by the dual Youla parameters are attenuated. The traditional fault-tolerant control (FTC) schemes under the Youla parameterisation often require the gradient information of the defined cost function for minimisation, which is either tedious or even unfeasible with unknown fault model. However, the proposed correlation-based FTC algorithm in this paper can compensate the faults via system data without the explicit fault model or the cost function gradient information. It is also proved that the algorithm convergence can be achieved without identifying the unknown fault model. For illustration, a simulation example with corresponding comparisons are presented to show the effectiveness of the proposed method in the end.     

基于参数灵敏度分析的交通仿真模型校正研究

为了使交通仿真模型校正工作能够高效开展,提出了以参数灵敏度分析为基础的模型校正框架。通过灵敏度分析确定影响模型精度的关键参数,以简化模型;对关键参数进行标定,以校正模型。以城市快速路交织区为仿真案例,以跟车模型和换道模型为研究对象;首先进行了大量仿真实验,分析不同车流量水平下模型参数的取值特征;据此制定模型参数的区间划分规则和交叉组合规则,从而对LH-OAT算法和遗传算法（GA）进行改进;然后应用改进LH-OAT算法（ILH-OAT）对模型参数进行灵敏度分析,再应用GA对关键参数进行标定;最后依据校验指标对仿真结果进行误差分析。结果表明ILH-OAT和GA相结合,不仅简化了仿真模型,降低了仿真运行成本,仿真效果也更加接近真实的道路交通运行情况。     

A fuzzy self-tuning parallel genetic algorithm for optimization

The genetic algorithm (GA) is now a very popular tool for solving optimization problems. Each operator has its special approach route to a solution. For example, a GA using crossover as its major operator arrives at solutions depending on its initial conditions. In other words, a GA with multiple operators should be more robust in global search. However, a multiple operator GA needs a large population size thus taking a huge time for evaluation. We therefore apply fuzzy reasoning to give effective operators more opportunity to search while keeping the overall population size constant. We propose a fuzzy self-tuning parallel genetic algorithm (FPGA) for optimization problems. In our test case FPGA there are four operators—crossover, mutation, sub-exchange, and sub-copy. These operators are modified using the eugenic concept under the assumption that the individuals with higher fitness values have a higher probability of breeding new better individuals. All operators are executed in each generation through parallel processing, but the populations of these operators are decided by fuzzy reasoning. The fuzzy reasoning senses the contributions of these operators, and then decides their population sizes. The contribution of each operator is defined as an accumulative increment of fitness value due to each operator's success in searching. We make the assumption that the operators that give higher contribution are more suitable for the typical optimization problem. The fuzzy reasoning is built under this concept and adjusts the population sizes in each generation. As a test case, a FPGA is applied to the optimization of the fuzzy rule set for a model reference adaptive control system. The simulation results show that the FPGA is better at finding optimal solutions than a traditional GA.     

A multi-objective Markov Chain Monte Carlo cellular automata model: Simulating multi-density urban expansion in NYC

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once.     

Design and characterization of cellular automata based associative memory for pattern recognition.

This paper reports a cellular automata (CA) based model of associative memory. The model has been evolved around a special class of CA referred to as generalized multiple attractor cellular automata (GMACA). The GMACA based associative memory is designed to address the problem of pattern recognition. Its storage capacity is found to be better than that of Hopfield network. The GMACA are configured with nonlinear CA rules that are evolved through genetic algorithm (GA). Successive generations of GA select the rules at the edge of chaos. The study confirms the potential of GMACA to perform complex computations like pattern recognition at the edge of chaos.     

A genetic algorithm for the vendor-managed inventory routing problem with lost sales

This paper proposes a genetic algorithm (GA) for the inventory routing problem with lost sales under a vendor-managed inventory strategy in a two-echelon supply chain comprised of a single manufacturer and multiple retailers. The proposed GA is inspired by the solving mechanism of CPLEX for the optimization model of the problem. The proposed GA determines replenishment times and quantities and vehicle routes in a decoupled manner, while maximizing supply chain profits. The proposed GA is compared with the optimization model with respect to the effectiveness and efficiency in various test problems. The proposed GA finds solutions in a short computational time that are very close to those obtained with the optimization model for small problems and solutions that are within 3.2% of those for large problems. Furthermore, sensitivity analysis is conducted to investigate the effects of several problem parameters on the performance of the proposed GA and total profits.     

The Ising genetic algorithm with Gibbs distribution sampling: Application to FIR filter design

In this paper the design of maximally flat linear phase finite impulse response (FIR) filters is considered. The problem with using the genetic algorithm (GA) in this kind of problems is the high cost of evaluating the fitness for each string in the population. The designing of optimum FIR filters under given constraints and required criteria includes exhaustive number of evaluations for filter coefficients, and the repetitive evaluations of objective functions that implicitly constitutes construction of the filter transfer functions. This problem is handled here with acceptable results utilizing Markov random fields (MRF's) approach. We establish a new theoretical approach here and we apply it on the design of FIR filters. This approach allows us to construct an explicit probabilistic model of the GA fitness function forming what is called the “Ising GA” that is based on sampling from a Gibbs distribution. Ising GA avoids the exhaustive design of suggested FIR filters (solutions) for every string of coefficients in every generation and replace this by a probabilistic model of fitness every gap (period) of iterations. Experimentations done with Ising GA of probabilistic fitness models are less costly than those done with standard GA and with high quality solutions.     

A novel stochastic optimization algorithm

This paper presents a new stochastic approach SAGACIA based on proper integration of simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) for solving complex optimization problems. SAGACIA combines the advantages of SAA, GA, and CA together. It has the following features: (1) it is not the simple mix of SAA, GA, and CA; (2) it works from a population; (3) it can be easily used to solve optimization problems either with continuous variables or with discrete variables, and it does not need coding and decoding,; and (4) it can easily escape from local minima and converge quickly. Good solutions can be obtained in a very short time. The search process of SAGACIA can be explained with Markov chains. In this paper, it is proved that SAGACIA has the property of global asymptotical convergence. SAGACIA has been applied to solve such problems as scheduling, the training of artificial neural networks, and the optimizing of complex functions. In all the test cases, the performance of SAGACIA is better than that of SAA, GA, and CA.     

Guided local search as a network planning algorithm that incorporates uncertain traffic demands

A search based heuristic for the optimisation of communication networks where traffic forecasts are uncertain and the problem is NP-complete is presented. While algorithms such as genetic algorithms (GA) and simulated annealing (SA) are often used for this class of problem, this work applies a combination of newer optimisation techniques specifically: fast local search (FLS) as an improved hill climbing method and guided local search (GLS) to allow escape from local minima. The GLS + FLS combination is compared with an optimised GA and SA approaches. It is found that in terms of implementation, the parameterisation of the GLS + FLS technique is significantly simpler than that for a GA and SA. Also, the self-regularisation feature of the GLS + FLS approach provides a distinctive advantage over the other techniques which require manual parameterisation. To compare numerical performance, the three techniques were tested over a number of network sets varying in size, number of switch circuit demands (network bandwidth demands) and levels of uncertainties on the switch circuit demands. The results show that the GLS + FLS outperforms the GA and SA techniques in terms of both solution quality and optimisation speed but even more importantly GLS + FLS has significantly reduced parameterisation time.     

CSMA/CA在CSMA/TDMA混合网络中的性能分析

CSMA/CA机制与TDMA在同频段共存时会存在相互干扰,为评估混合网络中CSMA/CA的性能表现,将TDMA视为周期性的干扰提出改进的二维Markov分析模型,能够计算不同条件下的CSMA/CA饱和吞吐量,并通过仿真实验证明了模型的有效性。同时讨论了TDMA时隙分配方式对系统性能产生的影响,仿真结果表明时隙均匀分配方式能保证较低的时延和时延抖动,而连续分配方式能使CSMA/CA获得较高的吞吐量。提出将基于最低信道需求时间的阈值计算方法与吞吐量分析模型相结合,用于在不同应用场景下进行时隙分配方式的选择。     

逆向物流供应链最佳供应商选择及订单量分配*

为解决逆向物流供应链中,供应商选择、订单量分配和提货点位置等不确定问题,建立了一个新的模糊多目标数学模型来确定最佳供应商选择、供应量及提货点位置,为避免在解决多目标模型时人为主观赋权,运用基于模糊目标规划的蒙特卡罗仿真模型来求解帕累托(pareto)理想解,采用遗传算法进行求解,并给出了相应优化方案,在此基础上研究讨论了不同权重分配下结果的优劣性及供应商选择风险,最后,针对不同权重分配,比较了遗传算法和Gurobi求解,实验表明,对于该问题模型遗传算法在解的优劣性上优于Gurobi。     

Efficient resource allocation for genetic algorithm based multi-objective optimization with 1,000 simulations

This study pertains to practical use of the GA for industrial applications where only a limited number of simulations can be afforded. Specifically, an attempt is made to find an efficient allocation of the total simulation budget (population size and number of generations) for constrained multi-objective optimization. A study is conducted to seek improvements while restricting the number of simulations to 1,000. Parallelization is exploited using concurrent simulations for each GA generation on a HP quad-core cluster, and resulted in a significant time savings. Furthermore, the efficient distribution of computational effort to achieve the greatest improvement in performance was explored. Two analytical examples as well as an automotive crashworthiness simulation of a finite element model with 58,000 elements were used as test examples. Various population sizes and numbers of generations were tried while limiting the total number of simulations to 1,000. The optimization performance was compared with Monte-Carlo and space filling sampling methods. It was observed that using the GA, many feasible and trade-off solutions could be found. It is shown that allowing a large number of generations is beneficial to get good trade-off solutions. For the vehicle design, significant improvements in the performance were observed. This example also suggests that, for problems with a small feasible region, the number of feasible solutions can be significantly increased in the first few generations involving about 200 simulations.     

Multi-criteria genetic optimisation of the manoeuvres of a two-stage launcher

The aim of this paper is to study the use of a genetic algorithm (GA) to optimise the ascent trajectory of a conventional two-stage launcher. The equations of motion of this system lack analytical solutions, and the number of adjustable parameters is large enough that the use of some non-traditional optimisation method becomes necessary. Two different missions are considered: first, to reach the highest possible stable, circular Low Earth Orbit (LEO); and second, to maximise the speed of a tangential escape trajectory. In this study, three variables are tuned and optimised by the GA in order to satisfy mission constraints while maximising the target function. The technical characteristics and limitations of the launcher are taken into account in the mission model, and a fixed payload weight is assumed. A variable mutation rate helps expand the search area whenever the population of solutions becomes uniform, and is shown to accelerate convergence of the GA in both cases. The obtained results are in agreement with technical specifications and solutions obtained in the past.     

Dual hierarchical genetic-optimal control: A new global optimal path planning method for robots

A new two-stage analytical-evolutionary algorithm considering dynamic equations is presented to find global optimal path. The analytical method is based on the indirect open loop optimal control problem and the evolutionary method is based on genetic algorithm (GA). Initial solutions, as start points of optimal control problem, are generated by GA to be used by optimal control. Then, a new sub-optimal path is generated through optimal control. The cost function is calculated for every optimal solution and the best solutions are chosen for the next step. The obtained path is used by GA to produce new generation of start points. This process continues until the minimum cost value is achieved. In addition, a new GA operator is introduced to be compatible with optimal control. It is used to select the pair chromosomes for crossover. The proposed method eliminates the problem of optimal control (being trapped in locally optimal point) and problem of GA (lack of compatibility with analytical dynamic equations). Hence problem is formulated and verification is done by comparing the results with a recent work in this area. Furthermore effectiveness of the method is approved by a simulation study for spatial non-holonomic mobile manipulators through conventional optimal control and the new proposed algorithm.     

A hybrid genetic algorithm for the electric vehicle routing problem with time windows

Driven by the newlegislation on greenhouse gas emissions, carriers began to use electric vehicles (EVs) for logistics transportation. This paper addresses an electric vehicle routing problem with time windows (EVRPTW). The electricity consumption of EVs is expressed by the battery state-of-charge (SoC). To make it more realistic, we take into account the terrain grades of roads, which affect the travel process of EVs. Within our work, the battery SoC dynamics of EVs are used to describe this situation. We aim to minimize the total electricity consumption while serving a set of customers. To tackle this problem, we formulate the problem as a mixed integer programming model. Furthermore, we develop a hybrid genetic algorithm (GA) that combines the 2-opt algorithm with GA. In simulation results, by the comparison of the simulated annealing (SA) algorithm and GA, the proposed approach indicates that it can provide better solutions in a short time.     

A hybrid estimation of distribution algorithm for simulation-based scheduling in a stochastic permutation flowshop

The permutation flowshop scheduling problem (PFSP) is NP-complete and tends to be more complicated when considering stochastic uncertainties in the real-world manufacturing environments. In this paper, a two-stage simulation-based hybrid estimation of distribution algorithm (TSSB-HEDA) is presented to schedule the permutation flowshop under stochastic processing times. To deal with processing time uncertainty, TSSB-HEDA evaluates candidate solutions using a novel two-stage simulation model (TSSM). This model first adopts the regression-based meta-modelling technique to determine a number of promising candidate solutions with less computation cost, and then uses a more accurate but time-consuming simulator to evaluate the performance of these selected ones. In addition, to avoid getting trapped into premature convergence, TSSB-HEDA employs both the probabilistic model of EDA and genetic operators of genetic algorithm (GA) to generate the offspring individuals. Enlightened by the weight training process of neural networks, a self-adaptive learning mechanism (SALM) is employed to dynamically adjust the ratio of offspring individuals generated by the probabilistic model. Computational experiments on Taillard’s benchmarks show that TSSB-HEDA is competitive in terms of both solution quality and computational performance.