On finding optimal and near-optimal lineal spanning trees

The search for good lineal, or depth-first, spanning trees is an important aspect in the implementation of a wide assortment of graph algorithms. We consider the complexity of findingoptimal lineal spanning trees under various notions of optimality. In particular, we show that several natural problems, such as constructing a shortest or a tallest lineal tree, are NP-hard. We also address the issue of polynomial-time, near-optimization strategies for these difficult problems, showing that efficient absolute approximation algorithms cannot exist unlessP = NP.This author's research was supported in part by the Sandia University Research Program and by the National Science Foundation under Grant M IP-8603879.This author's research was supported in part by the National Science Foundation under Grants ECS-8403859 and MIP-8603879.     

Experiences with a parallel algorithm for data flow analysis

We have designed a family of parallel data flow analysis algorithms for execution on distributed-memory MIMD machines, based on general-purpose, hybrid algorithms for data flow analysis [Marlowe and Ryder 1990]. We exploit a natural partitioning of the hybrid algorithms and explore a static mapping, dynamic scheduling strategy. Alternative mapping-scheduling choices and refinements of the flow graph condensation used are discussed. Our parallel hybrid algorithm family is illustrated on Reaching Definitions, although parallel algorithms also exist for many interprocedural (e.g., Aliasing) and intraprocedural (e.g., Available Expressions) problems [Marlowe 1989]. We have implemented the parallel hybrid algorithm for Reaching Definitions on an Intel iPSC/2. Our empirical results suggest the practicality of parallel hybrid algorithms.An earlier version of this paper was presented at Supercomputing '90.The research reported here was supported, in part, by the New Jersey Commission on Science and Technology and the CAIP Center's Industrial Members, by Siemens Research Corporation and by National Science Foundation grant CCR-8920078.     

Application of parallelized analogical planning to engineering design

Analogical planning provides a means of solving engineering problems where other machine learning methods fail. Unlike many machine learning paradigms, analogy does not require numerous previous examples or a rich domain theory. Instead, analogical planners adapt knowledge of solved problems in similar domains to the current problem. Unfortunately, the analogical planning task is an expensive one. While the process of forming correspondences between a known problem and a new problem is complex, the problem of selecting a base case for the analogy is virtually intractable.This paper addresses the issue of efficiently forming analogical plans. The Anagram planning system is described, which takes advantage of the massively parallel architecture of the Connection Machine to perform base selection and map formation. Anagram provides a tractable solution to analogical planning, with a complexity that is sublinear in the size of the plans.This paper describes the Anagram system and its parallel algorithms. The paper also presents theoretical analyses and empirical results of testing the system on a large database of plans from the domain of automatic programming.     

Surveillance test policy optimization through genetic algorithms using non-periodic intervention frequencies and considering seasonal constraints

In order to maximize systems average availability during a given period of time, it has recently been developed a non-periodic surveillance test optimization methodology based on genetic algorithms (GA). The fact of allowing non-periodic tests turns the solution space much more flexible and schedules can be better adjusted, providing gains in the overall system average availability, when compared to those obtained by an optimized periodic test scheme. This approach, however, turns the optimization problem more complex. Hence, the use of a powerful optimization technique, such as GA, is required.Considering that some particular features of certain systems can turn it advisable to introduce other specific constraints in the optimization problem, this work investigates the application of seasonal constraints for the set of the Emergency Diesel Generation of a typical four-loop pressurized water reactor in order to planning and optimizing its surveillance test policy. In this analysis, the growth of the blackout accident probability during summer, due to electrical power demand increases, was considered. Here, the used model penalizes surveillance test interventions when the blackout probability is higher.Results demonstrate the ability of the method in adapting the surveillance test policy to seasonal constraints. The knowledge acquired by the GA during the searching process has lead to test schedules that drastically minimize test interventions at periods of high blackout probability. It is compensated by more frequent redistributed tests through the periods of low blackout probability in order to improve on the overall average availability at the system level.     

Partitioning multiple objective optimal solutions with applications in radiotherapy design

The optimal partition for linear programming is induced by any strictly complementary solution, and this partition is important because it characterizes the optimal set. However, constructing a strictly complementary solution in the presence of degeneracy was not practical until interior point algorithms became viable alternatives to the simplex algorithm. We develop analogs of the optimal partition for linear programming in the case of multiple objectives and show that these new partitions provide insight into the optimal set (both pareto optimality and lexicographic ordering are considered). Techniques to produce these optimal partitions are provided, and examples from the design of radiotherapy plans show that these new partitions are useful. A. Holder. Research conducted at Trinity University, TX, and The University of Mississippi, MS. This research was partially supported by ONR Grant N00014-01-1-0917.     

基于遗传算法的工作辊温度场参数优化模型

针对热连轧机工作辊热辊形计算中温度场模型的热交换等参数难以确定的问题 ,建立了基于遗传算法的参数优化模型 ,可以解决复杂条件下的热参数的求解问题。利用优化参数计算的轧辊温度场与实际测量结果一致。     

Minimizing total weighted completion time when scheduling orders in a flexible environment with uniform machines

We consider the scheduling of orders in an environment with m uniform machines in parallel. Each order requests certain amounts of k different product types. Each product type can be produced by any one of the m machines. No setup is required if a machine switches over from one product type to another. Different product types intended for the same order can be produced at the same time (concurrently) on different machines. Each order is released at time zero and has a positive weight. Preemptions are allowed. The completion time of an order is the finish time of the product type that is completed last for that order. The objective is to minimize the total weighted completion time. We propose heuristics for the non-preemptive as well as the preemptive case and obtain worst case bounds that are a function of the number of machines as well as the differences in the speeds of the machines. Even though the worst-case bounds we showed for the two heuristics are not very tight, our experimental results show that they yield solutions that are very close to optimal.     

A Class of Algorithms for Collision Resolution with Multiplicity Estimation

The wireless connectivity in pervasive computing has ephemeral character and can be used for creating ad hoc networks, sensor networks, connection with RFID (Radio Frequency Identification) tags etc. The communication tasks in such wireless networks often involve an inquiry over a shared channel, which can be invoked for: discovery of neighboring devices in ad hoc networks, counting the number of RFID tags that have a certain property, estimating the mean value contained in a group of sensors etc. Such an inquiry solicits replies from possibly large number of terminals n. This necessitates the usage of algorithms for resolving batch collisions (conflicts) with unknown conflict multiplicity n. In this paper we present a novel approach to the problem of collision resolution for batch conflicts. We show how the conventional tree algorithms for collision resolution can be used to obtain progressively accurate estimation of the multiplicity. We use the estimation to propose a more efficient binary tree algorithm, termed Estimating Binary Tree (EBT) algorithm. The EBT algorithm is suited for implementation when the conflicting nodes are passive, such as e.g. RFID tags. We extend the approach to design the Interval Estimation Conflict Resolution (IECR) algorithm. For n→∞ we prove that the efficiency achieved by IECR for batch arrivals is identical with the efficiency that Gallager’s FCFS algorithm achieves for Poisson packet arrivals. For finite n, the simulation results show that IECR is, to the best of our knowledge, the most efficient batch resolution algorithm reported to date.     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

Algorithms for multiprocessor scheduling with two job lengths and allocation restrictions

A variant of the High Multiplicity Multiprocessor Scheduling Problem with C job lengths is considered, in which jobs can be processed only by machines not greater than a given index. When C=2, polynomial algorithms are proposed, for the feasibility version of the problem and for maximizing the number of scheduled jobs.