A dominance-based stepwise approach for sensor placement optimization

A Wireless Sensor Network (WSN) usually consists of numerous wireless devices deployed in a region of interest, each of which is capable of collecting and processing environmental information and communicating with neighboring devices. The problem of sensor placement becomes non trivial when we consider environmental factors such as terrain elevations. In this paper, we differentiate a stepwise optimization approach from a generic optimization approach, and show that the former is better suited for sensor placement optimization. Following a stepwise optimization approach, we propose a Crowd-Out Dominance Search (CODS), which makes use of terrain information and intersensor relationship information to facilitate the optimization. Finally, we investigate the effect of terrain irregularity on optimization algorithm performances, and show that the proposed method demonstrates better resistance to terrain complexity than other optimization methods.     

Pareto-based evolutionary computational approach for wireless sensor placement

Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple objectives be met. These objectives are usually conflicting, e.g. achieving maximum coverage and maximum connectivity while minimizing the network energy cost. A flexible algorithm for sensor placement (FLEX) is presented that uses an evolutionary computational approach to solve this multiobjective sensor placement optimization problem when the number of sensor nodes is not fixed and the maximum number of nodes is not known a priori. FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations. It keeps track of new genes through historical markings, which are used in later generations to assess two networks’ compatibility and also to align genes during crossover. It uses Pareto-dominance to approach Pareto-optimal layouts with respect to the objectives. Speciation is employed to aid the survival of gene innovations and facilitate networks to compete with similar networks. Elitism ensures that the best solutions are carried over to the next generation. The flexibility of the algorithm is illustrated by solving the device/node placement problem for different applications like facility surveillance, coverage with and without obstacles, preferential surveillance, and forming a clustering hierarchy.     

A case-based approach to heuristic planning

Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.     

A heuristic approach to commodity purchasing

In this paper a Heuristic model is proposed for commodity purchasing. The model is based on the combined use of Dynamic Programming model and a price trend forecasting model. The proposed approach is highly efficient and is simple to use even by a layman. Performance of the proposed model is compared with that of the Dynamic Programming model. The results of the sensitivity analysis indicate that the proposed approach performs consistently better compared to that of the Dynamic Programming model. It is. therefore, believed that the proposed approach would be fruitfully utilized in designing an optimal commodity purchasing system.     

A heuristic algorithm for optimal placement of rectangular objects

The problem of partitioning a two-dimensional area into pieces having certain sizes with a minimum of wasted space is very important, especially in packing components tightly in the manufacture of very large-scale integrated circuits. The purpose of this paper is to examine the problem of placing rectangular objects in a rectangular area so as to minimize the wasted space, from the viewpoint of establishing maximum empty rectangles rather than the standard linear-programming approach. A comparison of our results with those of the Steudel [5] is reported. Empirical comparisons of our results indicate that our algorithm is very simple and efficient.     

一种利用函数链神经网络的传感器建模新方法

讨论基于函数链神经网络 (FLNN)的传感器建模新方法 ,其结构简单、使用灵活、建模容易 ,易于实时硬件实现。两个算例说明网络的训练和非线性逼近方法 ,显示出网络的自适应能力、学习能力 ,基于FLNN的传感器模型可同时实现温度补偿和非线性校正。实际上 ,利用这种模型可以跟踪补偿环境改变引起的传感器特性的各种变化 ,在测控系统中具有良好的应用前景。     

A heuristic approach to automatic grasp planning for a 3-fingered hand

This paper presents the core of a software system able to determine a good grasp configuration on 3D objects for a three-fingered hand. The grasp planning problem has been studied considering both the constraints due to the stability and accessibility conditions, and the ones related to functionality. Physical, geometrical, spatial and task-related knowledge for solving the grasp planning problem have been properly modelled to support a heuristic-based reasoning process. A series of heuristic rules and geometric tests are used to scan the solution space, searching for a good grasp. In fact, when considering the three-dimensional case, a purely analytical and exhaustive approach appears too complex because of the dimension of the search space. This approach results in an incremental and modular model of grasp reasoning, that has been implemented using the Flex expert system shell. This work has been developed and demonstrated within the Esprit 2 project CIM-PLATO No. 2202.     

A novel heuristic approach for distance- and connectivity-based multihop node localization in wireless sensor networks

The availability of accurate location information of constituent nodes becomes essential in many applications of wireless sensor networks. In this context, we focus on anchor-based networks where the position of some few nodes are assumed to be fixed and known a priori, whereas the location of all other nodes is to be estimated based on noisy pairwise distance measurements. This localization task embodies a non-convex optimization problem which gets even more involved by the fact that the network may not be uniquely localizable, especially when its connectivity is not sufficiently high. To efficiently tackle this problem, we present a novel soft computing approach based on a hybridization of the Harmony Search (HS) algorithm with a local search procedure that iteratively alleviates the aforementioned non-uniqueness of sparse network deployments. Furthermore, the areas in which sensor nodes can be located are limited by means of connectivity-based geometrical constraints. Extensive simulation results show that the proposed approach outperforms previously published soft computing localization techniques in most of the simulated topologies. In particular, to assess the effectiveness of the technique, we compare its performance, in terms of Normalized Localization Error (NLE), to that of Simulated Annealing (SA)-based and Particle Swarm Optimization (PSO)-based techniques, as well as a naive implementation of a Genetic Algorithm (GA) incorporating the same local search procedure here proposed. Non-parametric hypothesis tests are also used so as to shed light on the statistical significance of the obtained results.     

A SIMD approach to parallel heuristic search

Serial search algorithms often exhibit exponential run times and may require an exponential amount of storage as well. Thus, the design of parallel search algorithms with limited memory is of obvious interest. This paper presents an efficient SIMD parallel algorithm, called IDPS (for iterative-deepening parallel search). At a broad level IDPS is a parallel version of IDA*. While generically we have called our algorithm an IDPS, performance of four variants of it has been studied through experiments conducted on the well-known test-bed problem for search algorithms, namely the Fifteen Puzzle. During the experiments, data were gathered under two different static load balancing schemes. Under the first scheme, an unnormalized average efficiency of approximately 3/4 was obtained for 4K, 8K, and 16K processors. Under the second scheme, unnormalized average efficiencies of 0.92 and 0.76, and normalized average efficiencies of 0.70 and 0.63 were obtained for 8K and 16K processors, respectively. We show (as shown previously only for MIMD machines) that for admissible search, high average speedup can be obtained for problems of significant size. We believe that this research will enhance AI problem solving using parallel heuristic search algorithms.     

A heuristic error-feedback learning algorithm for fuzzy modeling

Describes a type of fuzzy system with interpolating capability to extract MISO fuzzy rules from input-output sample data through learning. The proposed model inherits many merits from Sugeno-type models and their variations. A heuristic error-feedback learning algorithm associated with the model is suggested. Based on which, the estimator is shown to have a self-adjusting step when approaching a minimum     

A heuristic approach to necessary conditions of optimality

A simple, direct method is presented for deriving necessary conditions of optimality covering most of the situations encountered in practice. The method is based on the observation that, if generalized functions are admitted, a broad class of optimal control problems can be transcribed into a canonical form called the Canonical Variational Problem (CVP). The necessary optimality condition for CVP is phrased in the form of a Lagrange multiplier rule. This condition contains many of the results relating to minimum principles in the optimal control literature.     

A heuristic approach for transfer lines balancing

The paper deals with optimal balancing transfer lines where the operations in each workstation are grouped into blocks. All operations of the same block are executed simultaneously by one spindle head. Spindle heads of the same workstation are activated sequentially. The workstation time is the sum of the processing times of its blocks. The problem is to find the best assignment of operations to blocks and assignment of blocks to workstations that leads to the minimal transfer line cost (a weighted sum of blocks and workstation numbers). The solution must provide a desired productivity rate (cycle time). It must also satisfy precedence and compatibility constraints. Two heuristic algorithms based on the COMSOAL technique are proposed. Results from computer testing are reported.     

A heuristic approach to reinforcement learning control systems

This paper describes a learning control system using a reinforcement technique. The controller is capable of controlling a plant that may be nonlinear and nonstationary. The only a priori information required by the controller is the order of the plant. The approach is to design a controller which partitions the control measurement space into sets called control situations and then learns the best control choice for each control situation. The control measurements are those indicating the state of the plant and environment. The learning is accomplished by reinforcement of the probability of choosing a particular control choice for a given control situation. The system was stimulated on an IBM 1710-GEDA hybrid computer facility. Experimental results obtained from the simulation are presented.     

A new heuristic approach to assembly line balancing

The main feature of the method suggested in this paper is the assignment of priority to elements and priority elements are preferred to non-priority elements when assigning elements to stations. It gives the minimum number of stations under a predetermined cycle time. The work element time is considered to be invariant. This method has been tested by solving nearly all the problems available in the pertinent literature. This method yields better or similar results as available in the literature. A computer program incorporating the new heuristic method is presented in the paper.     

A dynamic programming approach to GA-based heuristic for multi-period CF problems

In this paper we have introduced a multi-period cell formation (CF) model which is more computationally challenging than the most comprehensive CF models in the literature. A dynamic programming (DP) based approach coupled with GA-based heuristic is proposed to solve the multi-period problem. Since, the introduced dynamic programming is general and can be applied to any GA-based heuristic with full rejuvenation cycles to solve the multi-period part of the model, we focused only on the DP approach in this paper but have explained the interface with the GA-based heuristic. Illustrative example has been provided that clarifies the application of DP-heuristic. The performance of the DP-heuristic has been evaluated against LINGO and multi period GA-based heuristic.     

A heuristic approach to solving a class of bilinear matrix inequality problems

Many canonical and modern control problems can be recast into the problem of a group of matrix inequalities. Some of them are in the form of linear matrix inequalities (LMIs), which can be solved very efficiently by the powerful LMI toolbox in Matlab, but some others are in the form of bilinear matrix inequalities. The characteristic of this latter class of problems is that when the so called “communicating variables” are fixed, the overall problem will be reduced to the problem in LMIs. Thus, how to find the communicating variables is the key to solve the whole problem. In this paper, an optimal estimate for the communicating variables is presented. We will illustrate our method by completely solving the problems of overshoot bound control and reachable set analysis for uncertain systems. Numerical examples are provided to show the effectiveness of the proposed method.     

A new heuristic approach for non-convex optimization problems

In this work a new optimization method, called the heuristic Kalman algorithm (HKA), is presented. This new algorithm is proposed as an alternative approach for solving continuous, non-convex optimization problems. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other metaheuristics, lies in the small number of parameters that need to be set by the user. Further, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA was evaluated in detail using several non-convex test problems, both in the unconstrained and constrained cases. The results were then compared to those obtained via other metaheuristics. The numerical experiments show that HKA is a promising approach for solving non-convex optimization problems, particularly in terms of computation time and success ratio.