A restart local search algorithm for solving maximum set k-covering problem

The maximum set k-covering problem (MKCP) is a famous combinatorial optimization problem with widely many practical applications. In our work, we design a restart local search algorithm for solving MKCP, which is called RNKC. This algorithm effectively makes use of several advanced ideas deriving from the random restart mechanism and the neighborhood search method. RNKC designs a new random restart method to deal with the serious cycling problem of local search algorithms. Thanks to the novel neighborhood search method that allows a neighborhood exploration of as many feasible search areas as possible, the RNKC can obtain some greatly solution qualities. Comprehensive results on the classical instances show that the RNKC algorithm competes very favorably with a famous commercial solver CPLEX. In particular, it discovers some improved and great results and matches the same solution quality for some instances.

     

The parsing problem for affix grammars

Summary Affix grammars are an extension of context-free grammars which retain most of their advantages and eliminate most of their limitations with respect to the definition of programming languages and the specification of their translators. The extension allows definition of context-sensitive syntax features, and also allows semantics to be linked to syntax. In this paper, the parsing problem for affix grammars is explored and shown to be closely related to the parsing problem for context-free grammars. This enables a standard context-free parser constructor to be generalised to a constructor for affix grammars, essentially by addition of a preprocessor. The resulting constructors are compared with previously implemented or proposed constructors.     

The covering problem for linear context-free grammars

Language equivalence, grammatical covering and structural equivalence are all notions of similarity defined on context-free grammars. We show that the problem of determining whether an arbitrary linear context-free grammar covers another is complete for the class of languages accepted by polynomially space bounded Turing machines. We then compare the complexity of this problem with the analogous problems for language equivalence and structural equivalence, not only for linear grammars, but also for regular grammars and unrestricted context-free grammars. As a step in obtaining the main result of this paper, we show that the equivalence problem for linear s-grammars is decidable in polynomial time.     

Chameleon: a system for solving the data-translation problem

A comprehensive data translation system is described with the following characteristics: (1) it is derived from a formal model of the translation task; (2) it supports the building of translation tools; (3) it supports the use of translation tools; and (4) it is accessible to its targeted end users. A software architecture to achieve the translation capability is fully implemented. Translators have been generated using the architecture, both by the original software developers and by industrial associates who have installed the architecture at their own sites     

The language intersection problem for non-recursive context-free grammars

We prove that, given as input two context-free grammars, deciding non-emptiness of intersection of the two generated languages is PSPACE-complete if at least one grammar is non-recursive. The problem remains PSPACE-complete when both grammars are non-recursive and deterministic. Also investigated are generalizations of the problem to several context-free grammars, of which a certain number are non-recursive.     

A new approach for solving set covering problem using jumping particle swarm optimization method

The set covering problem (SCP) is a well known classic combinatorial NP-hard problem, having practical application in many fields. To optimize the objective function of the SCP, many heuristic, meta heuristic, greedy and approximation approaches have been proposed in the recent years. In the development of swarm intelligence, the particle swarm optimization is a nature inspired optimization technique for continuous problems and for discrete problems we have the well known discrete particle swarm optimization (DPSO) method. Aiming towards the best solution for discrete problems, we have the recent method called jumping particle swarm optimization (JPSO). In this DPSO the improved solution is based on the particles attraction caused by attractor. In this paper, a new approach based on JPSO is proposed to solve the SCP. The proposed approach works in three phases: for selecting attractor, refining the feasible solution given by the attractor in order to reach the optimality and for removing redundancy in the solution. The proposed approach has been tested on the benchmark instances of SCP and compared with best known methods. Computational results show that it produces high quality solution in very short running times when compared to other algorithms.     

禁忌遗传算法求解最小支配集

如何寻找一个网络图的最小支配集是NP难题。分别设计了逆序启发式算法和禁忌搜索算法,并在此基础上提出了禁忌遗传算法（TSGA）用于求解最小支配集;将禁忌搜索和遗传算法结合起来,弥补了彼此的不足,既有效地避免了算法易陷入局部最优解的缺陷,又加快了算法的收敛速度。经对大量随机网络图的测试和对物流网络选址问题的求解,验证了TSGA算法的优越性。     

Negotiation as a metaphor for distributed problem solving

We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques.We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation.We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another.The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area.From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with planner, conniver, hearsay-ii, and pup6 are used to demonstrate that negotiation—the two-way transfer of information—is a natural extension to the transfer of control mechanisms used in earlier problem-solving systems.     

The bounded degree problem for NLC grammars is decidable

The degree of a graph H is the maximum among the degrees of its nodes. A set of graphs L is of bounded degree if there exists a positive integer n such that the degree of each graph in L does not exceed n. We demonstrate that it is decidable whether or not the (graph) language of an arbitrary node label controlled (NLC) grammar is of bounded degree. Moreover, it is shown that, given an arbitrary NLC grammar G generating the language L(G) of bounded degree, one can effectively compute the maximum integer which appears as the degree of a graph in L(G).     

Using XQuery for problem solving

XQuery is a recommendation by the World Wide Web Consortium as a standard XML query language. In addition to being a special‐purpose query language, XQuery has also features that support unexpected applications such as problem solving. We demonstrate and discuss these features by presenting several XQuery programs for solving recreational problems and puzzles. Copyright © 2011 John Wiley & Sons, Ltd.     

Vivid: A framework for heterogeneous problem solving

We introduce Vivid, a domain-independent framework for mechanized heterogeneous reasoning that combines diagrammatic and symbolic representation and inference. The framework is presented in the form of a family of denotational proof languages (DPLs). We present novel formal structures, called named system states, that are specifically designed for modeling potentially underdetermined diagrams. These structures allow us to deal with incomplete information, a pervasive feature of heterogeneous problem solving. We introduce a notion of attribute interpretations that enables us to interpret first-order relational signatures into named system states, and develop a formal semantic framework based on 3-valued logic. We extend the assumption-base semantics of DPLs to accommodate diagrammatic reasoning by introducing general inference mechanisms for the valid extraction of information from diagrams, and for the incorporation of sentential information into diagrams. A rigorous big-step operational semantics is given, on the basis of which we prove that the framework is sound. We present examples of particular instances of Vivid in order to solve a series of problems, and discuss related work.     

The word problem for visibly pushdown languages described by grammars

Visibly pushdown languages are an interesting subclass of deterministic context-free languages that can model nonregular properties of interest in program analysis. Such class properly contains typical classes of parenthesized languages such as “parenthesis”, “bracketed”, “balanced” and “input-driven” languages. It is closed under boolean operations and has decidable decision problems such as emptiness, inclusion and universality. We study the membership problem for visibly pushdown languages, and show that it can be solved in time linear in both the size of the input grammar and the length of the input word. The algorithm relies on a reduction to the reachability problem for game graphs. We also discuss the time complexity of the membership problem for the class of balanced languages which is the largest among those cited above. Besides the intrinsic theoretical interest, we further motivate our main result showing an application to the validation of XML documents against Schema and Document Type Definitions (DTDs). Work partially supported by funds for the research from MIUR 2006, grant “Metodi Formali per la verifica di sistemi chiusi ed aperti”, Università di Salerno. A preliminary version of this paper was published in the Proceedings of the 4th International Symposium Automated Technology for Verification and Analysis (ATVA 2006), Lecture Notes in Computer Science 4218, pp. 96–109, 2006.     

Iterative functional modification method for solving a transportation problem

We propose a new method for solving transportation problems based on decomposing the original problem into a number of two-dimensional optimization problems. Since the solution procedure is integer-valued and monotonic in the objective function, the required computation is finite. As a result, we get not only a single optimal solution of the original transportation problem but a system of constraints that can yield all optimal solutions. We give numerical examples that illustrate the constructions of our algorithm.     

A fuzzy approach for solving a critical benchmarking problem

Nowadays, benchmarking is a widespread technique for evaluating an aspect—process, product, service, etc.—by comparing it against the best in class with the aim of improving this aspect or identifying the best alternative. There have been numerous attempts at defining a rigorous benchmarking process by specifying steps that should be taken to put benchmarking into practice. All these proposals use a method of calculation that treats the weights and ratings of each criterion as numerical variables, even if they are not. This means that the binary and linguistic variables have to be artificially translated to numerical variables, misleading us into thinking that the concepts we are dealing with are quantitative when they really are not. In this paper, we propose a new method of calculation based on fuzzy logic to rectify this key methodological error. Its definition is based on: (i) a new division operator for fuzzy numbers representing conjugated variables, as in the case outlined here; (ii) a new aggregation operator that can integrate binary, numerical and/or linguistic variables; and, finally, (iii) an operator that can translate the final fuzzy rating into the linguistic variable that best represents it. Therefore, the resulting method is: (i) closer to the user since it manages more human-understandable values and (ii) not dependent on the above artificial translation process, which could lead to sizeable variations in the benchmarking result.     

An efficient column-generation-based algorithm for solving a pickup-and-delivery problem

We introduce a new variation of the pickup-and-delivery problem. Current methods for solving this problem rely on column-generation subroutines embedded in a branch-and-bound tree. Yet, when applied to our problem, these techniques suffer from significant combinatorial explosion in the number of routes generated by the column-generation subroutine and the number of nodes explored in the branch-and-bound tree. In this paper, by exploiting the problem structure, we develop a specialized column-generation subroutine that reduces the combinatorial explosion significantly leading to a more efficient procedure to solve the problem.