Extending the effectiveness of simulation-based DSS through genetic algorithms

Many real life ill-structured problems involve high uncertainty and complexity preventing application of analytical optimization techniques in building effective decision support systems (DSS). These systems may employ simulation method and search for a “good” solution through “what-if” analysis. However, this method is very time consuming and often overlooks the consideration of many promising alternative solutions. A genetic algorithm (GA) automates the search for “good” solutions by finding near-optimal solutions and increases effectiveness of DSS. This paper introduces a hybrid method based on the combination of Monte-Carlo simulation and genetic algorithms. The combined method is illustrated through application to the marketing mix problem to improve the process for searching and evaluating alternatives for decisional support. The paper compares two methods: MC and MC+GA. It also discusses ways for dealing with crisp and soft constraints contained in the example problem. A business game environment is chosen for experiments. The results of the experiments show that the GA-based approach outperforms human “what-if” method in terms of effectiveness and efficiency.     

一种广义空间调制系统的低复杂度检测算法

针对广义空间调制(GSM)系统中信号检测复杂度过高的问题,提出一种采用分组检测方式的低复杂度检测算法。首先发送端根据激活天线数对发射天线进行分组,每组激活一根天线用于传输调制符号,然后提出算法基于这种发射天线组合方式,在接收端做相应的分组串行检测。分析和仿真结果表明,该检测算法能以极低的检测复杂度获得与最大似然检测算法(MLD)相近的误比特率(BER)性能。     

An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems.

In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.     

Turbo equalization for block fading MIMO channels using random signal mapping

We present a novel turbo equalization scheme for block fading MIMO channels, where diversity is achieved using random signal mapping. We show that the computational complexity of the proposed scheme is not a function of the number of transmit antennas, and compares favorably to the complexity of similar systems based on space-time trellis codes and space-time block codes. Finally, we provide simulation results showing that the proposed scheme achieves full diversity, and investigate its performance in terms of the number of transmit antennas, ISI length and imperfect channel knowledge.     

A quick search method for audio and video signals based on histogram pruning

This paper proposes a quick method of similarity-based signal searching to detect and locate a specific audio or video signal given as a query in a stored long audio or video signal. With existing techniques, similarity-based searching may become impractical in terms of computing time in the case of searching through long-running (several-days' worth of) signals. The proposed algorithm, which is referred to as time-series active search, offers significantly faster search with sufficient accuracy. The key to the acceleration is an effective pruning algorithm introduced in the histogram matching stage. Through the pruning, the actual number of matching calculations can be reduced by 200 to 500 times compared with exhaustive search while guaranteeing exactly the same search result. Experiments show that the proposed method can correctly detect and locate a 15-s signal in a 48-h recording of TV broadcasts within 1 s, once the feature vectors are calculated and quantized. As extentions of the basic algorithm, efficient AND/OR search methods for searching for multiple query signals and a feature dithering method for coping with signal distortion are also discussed.     

一种低复杂度高性能的MIMO系统自适应检测算法

如何克服发射信号的重叠和码间干扰是MIMO系统信号检测技术面临的关键问题。信号检测算法的性能优劣是影响MIMO技术能否真正适于实际应用的关键因素。结合MLD算法高性能和MMSE-SIC算法低复杂的优点,对Hybrid算法进行了改进,提出了一种基于信道最大/最小特征值的自适应混合检测算法。该算法重新定义了自适应系数,并通过信道矩阵特征值的特性,自适应控制三种子混合算法检测数据流时的百分比,以达到更高的检测效率。仿真结果表明：无论信道在何种复杂环境下,该算法具有与MLD算法几乎相同的误码性能,计算复杂度也有很大的改善。     

基于Sorenson相似系数的快速天线选择算法

穷举搜索天线选择算法的计算复杂度随天线的增多变大,影响实际应用。针对该问题,提出基于Sorenson相似系数的快速天线选择算法。利用Sorenson相似系数来表征信道矩阵行向量间的相关性,通过逐行递增的方法,选择相似系数最小且行范数最大的接收天线,从而最大程度地增加系统容量。仿真结果表明,该算法在射频链路较少时计算复杂度很低,且能获得接近最优算法的中断容量。     

A joint antenna and user selection scheme for multiuser MIMO system

In this paper, we present a low-complexity algorithm for real-time joint user scheduling and receive antenna selection (JUSRAS) in multiuser MIMO systems. The computational complexity of exhaustive search for JUSRAS problem grows exponentially with the number of users and receives antennas. We apply binary particle swarm optimization (BPSO) to the joint user scheduling and receive antenna selection problem. In addition to applying the conventional BPSO to JUSRAS, we also present a specific improvement to this population-based heuristic algorithm; namely, we feed cyclically shifted initial population, so that the number of iterations until reaching an acceptable solution is reduced. The proposed BPSO for JUSRAS problem has a low computational complexity, and its effectiveness is verified through simulation results.     

基于量子Grover算法的MIMO检测技术

最大似然译码(MLD)是MIMO系统中最佳接收算法,但是其运算计算量随发射天线数呈指数增长,这是一个NP问题,如果利用量子并行处理的优势,将量子搜索算法应用于MIMO系统的检测中去,会有效地解决以上问题,提高系统的性能.提出了基于量子Grover算法的MIMO检测方案,并分析了该方案的性能和特点.     

Concurrent assembly planning with genetic algorithms

This work investigates the application of genetic algorithm (GA)-based search techniques to concurrent assembly planning, where product design and assembly process planning are performed in parallel, and the evaluation of a design configuration is influenced by the performance of its related assembly process. Several types of GAs and an exhaustive combinatorial approach are compared, in terms of reliability and speed in locating the global optimum. The different algorithms are tested first on a set of artificially generated assembly planning problems, which are intended to represent a broad spectrum of combinatorial complexity; then an industrial case study is presented. Test problems indicate that GAs are slightly less reliable than the combinatorial approach in finding the global, but are capable of identifying solutions which are very close to the global optimum with consistency, soon outperforming the combinatorial approach in terms of execution times, as the problem complexity grows. For an industrial case study of low combinatorial complexity, such as the one chosen in this work, GAs and combinatorial approach perform almost equivalently, both in terms of reliability and speed. In summary, GAs seem a suitable choice for those planning applications where response time is an important factor, and results which are close enough to the global optimum are still considered acceptable such as in concurrent assembly planning, where response time is a key factor when assessing the validity of a product design configuration in terms of the performance of its assembly plan.     

Low-complexity joint transmit and receive antenna selection for MIMO systems

In this paper, we present a low-complexity algorithm for real-time joint transmit and receive antenna selection in MIMO systems. The computational complexity of exhaustive search in this problem grows exponentially with the number of transmit and receive antennas. We apply Binary Particle Swarm Optimization (BPSO) to the joint transmit and receive antenna selection problem. In addition, applying the general BPSO to joint antenna selection, we also present a specific improvement to this population-based heuristic algorithm, namely, we feed cyclically shifted initial population so that the average convergence time (the number of iterations until reaching an acceptable solution) is reduced. The proposed BPSO for joint antenna selection problem has a low computational complexity, and its effectiveness is verified through simulation results.     

A comparative study of heuristic algorithms: GA and UMDA in spatially multiplexed communication systems

A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper. While the optimal maximum likelihood (ML) decoder using an exhaustive search method is prohibitively complex, simulation results show that the GA and UMDA optimized MIMO detection algorithms result in near optimal bit error rate (BER) performance with significantly reduced computational complexity. The results also suggest that the heuristic based MIMO detection outperforms the vertical bell labs layered space time (VBLAST) detector without severely increasing the detection complexity. The performance of UMDA is found to be superior to that of GA in terms of computational complexity and the BER performance.     

Optimal fuzzy control of exponential synchronisation via genetic algorithm

In this study, a novel approach via GA-based fuzzy control is proposed to realize the exponential optimal H^∞ synchronisation of MTDC systems. A robustness design of model-based fuzzy control is first presented to overcome the effect of modelling errors between the MTDC systems and T-S fuzzy models. Next, a delay-dependent exponential stability criterion is derived in terms of Lyapunov's direct method to guarantee that the trajectories of the slave system can approach those of the master system. Subsequently, the stability conditions of this criterion are reformulated into LMIs. According to the LMIs, a fuzzy controller is then synthesised to exponentially stabilise the error systems. Moreover, the capability of GA in random search for near-optimal solutions, the lower and upper bounds of the search space based on the feedback gains via LMI approach can be set so that the GA will seek better feedback gains of fuzzy controllers to speed up the synchronisation. Additionally, an IGA was proposed to overcome both the shortcomings of premature convergence of GA and local search. According to the IGA, a fuzzy controller is synthesised not only to realise the exponential synchronisation but also to achieve the optimal H^∞ performance by minimising the disturbance attenuation level.     

A fast recursive algorithm based on fuzzy 2-partition entropy approach for threshold selection

The fuzzy c-partition entropy approach for threshold selection is an effective approach for image segmentation. The approach models the image with a fuzzy c-partition, which is obtained using parameterized membership functions. The ideal threshold is determined by searching an optimal parameter combination of the membership functions such that the entropy of the fuzzy c-partition is maximized. It involves large computation when the number of parameters needed to determine the membership function increases. In this paper, a recursive algorithm is proposed for fuzzy 2-partition entropy method, where the membership function is selected as S-function and Z-function with three parameters. The proposed recursive algorithm eliminates many repeated computations, thereby reducing the computation complexity significantly. The proposed method is tested using several real images, and its processing time is compared with those of basic exhaustive algorithm, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and simulated annealing (SA). Experimental results show that the proposed method is more effective than basic exhaustive search algorithm, GA, PSO, ACO and SA.     

Optimization of process route by Genetic Algorithms

Process route sequencing is considered as the key technology for computer aided process planning (CAPP) and is very complex and difficult. In this paper, based on the analyzing of various constraints in process route sequencing and the astringency of Genetic Algorithms (GAs), the GA is reconstructed, including the establishing of the coding strategy, the evaluation operator and the fitness function. The new GAs can meet the requirement of sequencing work and can meet the requirement of astringency. The natural number is adopted in coding strategy, the “elitist model” and the “tournament selection” are adopted as selection operators, the nonconforming sequential searching crossover operator is used and the inconsistent mutation operator is adopted, the fitness function is defined as a formula of the sum of compulsive constraints with each weighing, and these constraints are used as the control strategy for GAs in the searching process. By using GAs in the optimization, the optimal or near-optimal process route is obtained finally.     

Optimal adaptive channel scheduling for scalable video broadcasting over MIMO wireless networks

Video broadcasting is an efficient way to deliver video content to multiple receivers. However, due to heterogeneous channel conditions in MIMO wireless networks, it is challenging for video broadcasting to map scalable video layers to proper MIMO transmit antennas to minimize the average overall video transmission distortion. In this paper, we investigate the channel scheduling problem for broadcasting scalable video content over MIMO wireless networks. An adaptive channel scheduling based unequal error protection (UEP) video broadcasting scheme is proposed. In the scheme, video layers are protected unequally by being mapped to appropriate antennas, and the average overall distortion of all receivers is minimized. We formulate this scheme into a non-linear combinatorial optimization problem. It is not practical to solve the problem by an exhaustive search method with heavy computational complexity. Instead, an efficient branch-and-bound based channel scheduling algorithm, named TBCS, is developed. TBCS finds the global optimal solution with much lower complexity. The complexity is further reduced by relaxing the termination condition of TBCS, which produces a (1 − ε)-optimal solution. Experimental results demonstrate both the effectiveness and efficiency of our proposed scheme and algorithm. As compared with some existing channel scheduling methods, TBCS improves the quality of video broadcasting across all receivers significantly.     

一种低复杂度的线性离散码发射天线选择技术

为了充分利用多天线系统的性能增益,通过研究集中式MIMO(Multiple input multiple output)系统中最大化最小后验SNR (Signal to noise ratio)准则的局限性,提出了一种低复杂度的基于线性离散码的发射天线选择方案.然后分析了分布式MIMO系统中,移动台距不同基站间不同的大尺度衰落对信道特征值的影响,证明了所提天线选择准则在分布式系统中的有效性.仿真结果表明,在准静态信道环境下,所提天线选择准则具有比最大化最小后验SNR准则更好的性能,并且复杂度更低.     

Time-line based model for software project scheduling with genetic algorithms

Effective management of complex software projects depends on the ability to solve complex, subtle optimization problems. Most studies on software project management do not pay enough attention to difficult problems such as employee-to-task assignments, which require optimal schedules and careful use of resources. Commercial tools, such as Microsoft Project, assume that managers as users are capable of assigning tasks to employees to achieve the efficiency of resource utilization, while the project continually evolves. Our earlier work applied genetic algorithms (GAs) to these problems. This paper extends that work, introducing a new, richer model that is capable of more realistically simulating real-world situations. The new model is described along with a new GA that produces optimal or near-optimal schedules. Simulation results show that this new model enhances the ability of GA-based approaches, while providing decision support under more realistic conditions.     

Genetic Algorithms for Project Management

The scheduling of tasks and the allocation of resource in medium to large-scale development projects is an extremely hard problem and is one of the principal challenges of project management due to its sheer complexity. As projects evolve any solutions, either optimal or near optimal, must be continuously scrutinized in order to adjust to changing conditions. Brute force exhaustive or branch-and-bound search methods cannot cope with the complexity inherent in finding satisfactory solutions to assist project managers. Most existing project management (PM) techniques, commercial PM tools, and research prototypes fall short in their computational capabilities and only provide passive project tracking and reporting aids. Project managers must make all major decisions based on their individual insights and experience, must build the project database to record such decisions and represent them as project nets, then use the tools to track progress, perform simple consistency checks, analyze the project net for critical paths, etc., and produce reports in various formats such as Gantt or Pert charts.Our research has developed a new technique based on genetic algorithms (GA) that automatically determines, using a programmable goal function, a near-optimal allocation of resources and resulting schedule that satisfies a given task structure and resource pool. We assumed that the estimated effort for each task is known a priori and can be obtained from any known estimation method such as COCOMO. Based on the results of these algorithms, the software manager will be able to assign tasks to staff in an optimal manner and predict the corresponding future status of the project, including an extensive analysis on the time-and-cost variations in the solution space. Our experiments utilized Wall's GALib as the search engine. The algorithms operated on a richer, refined version of project management networks derived from Chao's seminal work on GA-based Software Project Management Net (SPMnet). Generalizing the results of Chao's solution, the new GA algorithms can operate on much more complex scheduling networks involving multiple projects. They also can deal with more realistic programmatic and organizational assumptions. The results of the GA algorithm were evaluated using exhaustive search for five test cases. In these tests our GA showed strong scalability and simplicity. Its orthogonal genetic form and modularized heuristic functions are well suited for complex conditional optimization problems, of which project management is a typical example.     

Reduced complexity list sphere decoding for MIMO systems

Depth-first sphere decoding of MIMO systems has near maximum likelihood performance with reasonable computational complexity. In this paper, lower complexity depth-first sphere decoding and list sphere decoding algorithms are proposed. Several criteria for re-ordering the search dimensions are proposed. The proposed sphere decoders are shown to have a significantly reduced decoding complexity at low SNRs. To further reduce the complexity at high SNRs, the point search-space at each ordered dimension is adaptively reduced. Further reductions in the decoding complexity are achieved by inter-layer interference cancellation. It is shown that the proposed sphere decoding algorithms maintain their near-optimal performance, concurrently with a significant complexity reduction, over a wide SNR range.