Showing papers on "Heuristic (computer science) published in 1995"
TL;DR: This paper defines the various components comprising a GRASP and demonstrates, step by step, how to develop such heuristics for combinatorial optimization problems.
Abstract: Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.
2,370 citations
09 Jul 1995
TL;DR: Ant-Q algorithms were inspired by work on the ant system (AS), a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies and are applied to the solution of symmetric and asymmetric instances of the traveling salesman problem.
Abstract: In this paper we introduce Ant-Q, a family of algorithms which present many similarities with Q-learning (Watkins, 1989), and which we apply to the solution of symmetric and asymmetric instances of the traveling salesman problem (TSP). Ant-Q algorithms were inspired by work on the ant system (AS), a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in (Dorigo, 1992; Dorigo, Maniezzo and Colorni, 1996). We show that AS is a particular instance of the Ant-Q family, and that there are instances of this family which perform better than AS. We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP's are competitive with those obtained by other heuristic approaches based on neural networks or local search. Finally, we apply Ant-Q to some difficult asymmetric TSP's obtaining very good results: Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms.
668 citations
TL;DR: The design of computational experiments to test heuristic methods and reporting guidelines for such experimentation are discussed and the goal is to promote thoughtful, well-planned, and extensive testing of heuristics and integrity in and reproducibility of the reported results.
Abstract: This article discusses the design of computational experiments to test heuristic methods and provides reporting guidelines for such experimentation. The goal is to promote thoughtful, well-planned, and extensive testing of heuristics, full disclosure of experimental conditions, and integrity in and reproducibility of the reported results.
477 citations
Proceedings Article•
20 Aug 1995
TL;DR: This work introduces compound operators that dynamically change the topology of the search space to better utilize the information available from the evaluation of feature subsets and shows that compound operators unify previous approaches that deal with relevant and irrelevant features.
Abstract: In the wrapper approach to feature subset selection, a search for an optimal set of features is made using the induction algorithm as a black box. The estimated future performance of the algorithm is the heuristic guiding the search. Statistical methods for feature subset selection including forward selection, backward elimination, and their stepwise variants can be viewed as simple hill-climbing techniques in the space of feature subsets. We utilize best-first search to find a good feature subset and discuss overfitting problems that may be associated with searching too many feature subsets. We introduce compound operators that dynamically change the topology of the search space to better utilize the information available from the evaluation of feature subsets. We show that compound operators unify previous approaches that deal with relevant and irrelevant features. The improved feature subset selection yields significant improvements for real-world datasets when using the ID3 and the Naive-Bayes induction algorithms.
358 citations
TL;DR: This paper compares some of the most efficient heuristic methods for the quadratic assignment problem and shows that no one method is better than all the others.
332 citations
01 Jan 1995
TL;DR: In this article, the authors proposed a greedy genetic algorithm for the quadratic assignment problem (QAP), which incorporates many greedy principles in its design and hence, is called the greedy GA.
Abstract: The Quadratic Assignment Problem (QAP) is one of the classical combinatorial optimization problems and is known for its diverse applications. Applications of QAP include assignments of tools to robots in flexible manufacturing systems, allocation of blades to hydraulic turbine runners, sequencing problems in production systems, and placement problem in VLSI design. In this paper, we suggest a genetic algorithm for the QAP and report its computational behaviour. The genetic algorithm incorporates many greedy principles in its design and hence, is called the greedy genetic algorithm. The ideas we incorporate in the greedy genetic algorithm include (i) generating the initial population using a randomized construction heuristic; (ii) new crossover schemes; (iii) a special purpose immigration scheme that promotes diversity; (iv) periodic local optimization of a subset of the population; (v) tournamenting among different populations; and (vi) an overall design that attempts to strike a balance between diversity and a bias towards fitter individuals. We test our algorithm on all the benchmark instances of QAPLIB, a well-known library of QAP instances. Out of the 132 total instances in QAPLIB of varied sizes, the greedy genetic algorithm obtained the best known solutions for 103 instances, and for the remaining instances (except one) found solutions within 1% of the best known solutions. Based on our computational testing, we believe that the greedy genetic algorithm is the best heuristic algorithm for dense QAP developed to date in terms of the quality of the solution.
327 citations
TL;DR: A solution algorithm REBUS based on an insertion heuristics was developed, implemented in a dynamic environment intended for on-line scheduling, which permits in a flexible way weighting of the various goals such that the solution reflects the user's preferences.
Abstract: The paper describes a system for the solution of a static dial-a-ride routing and scheduling problem with time windows (DARPTW). The problem statement and initialization of the development project was made by the Copenhagen Fire-Fighting Service (CFFS). The CFFS needed a new system for scheduling elderly and disabled persons, involving about 50.000 requests per year. The problem is characterized by, among other things, multiple capacities and multiple objectives. The capacities refer to the fact that a vehicle may be equipped with e.g. normal seats, children seats or wheel chair places. The objectives relate to a number of concerns such as e.g. short driving time, high vehicle utilization or low costs. A solution algorithm REBUS based on an insertion heuristics was developed. The algorithm permits in a flexible way weighting of the various goals such that the solution reflects the user's preferences. The algorithm is implemented in a dynamic environment intended for on-line scheduling. Thus, a new request for service is treated in less than 1 second, permitting an interactive user interface.
312 citations
02 Apr 1995
TL;DR: Depending on how tight the delay bounds are, the costs of the multicast trees obtained with the new algorithm are shown to be very close to the cost of the trees obtained by the Kou, Markowsky and Berman's algorithm (1981).
Abstract: A new heuristic algorithm is presented for constructing minimum-cost multicast trees with delay constraints. The new algorithm can set variable delay bounds on destinations and handles two variants of the network cost optimization goal: one minimizing the total cost (total bandwidth utilization) of the tree, and another minimizing the maximal link cost (the most congested link). Instead of the single-pass tree construction approach used in most previous heuristics, the new algorithm is based on a feasible search optimization method which starts with the minimum-delay tree and monotonically decreases the cost by iterative improvement of the delay-bounded tree. The optimality of the costs of the delay-bounded trees obtained with the new algorithm is analyzed by simulation. Depending on how tight the delay bounds are, the costs of the multicast trees obtained with the new algorithm are shown to be very close to the costs of the trees obtained by the Kou, Markowsky and Berman's algorithm (1981).
297 citations
TL;DR: A more general model and efficient heuristic algorithms are developed to handle more realistic situation where link flow interaction cannot be ignored and can be used as efficient approaches for the bilevel O-D matrix estimation problems.
Abstract: Recently, a bilevel programming approach has been used for estimation of origin-destination (O-D) matrix in congested networks This approach integrates the conventional generalized least squares estimation model and the standard network equilibrium model into one process We extend this approach and develop a more general model and efficient heuristic algorithms to handle more realistic situation where link flow interaction cannot be ignored The extended model is formulated in the form of a bilevel programming problem with variational inequality constraints The upper-level problem seeks to minimize the sum of error measurements in traffic counts and O-D matrices, while the lower-level problem represents a network equilibrium problem formulated as variational inequalities, which guarantees that the estimated O-D matrix and corresponding link flows satisfy the network equilibrium conditions Two computational techniques are presented for solving the bilevel O-D matrix estimation model One is a heuristic iterative algorithm between traffic assignment and O-D matrix estimation and the other one is a sensitivity analysis based heuristic algorithm Properties of the two algorithms are analyzed theoretically and compared numerically with small network examples It is concluded that both algorithms can be used as efficient approaches for the bilevel O-D matrix estimation problems
292 citations
TL;DR: A review of the various solution methods for the capacitated plant location problem is provided in this article, where heuristic and exact procedures that have appeared in the literature are covered, and two innovative concepts, the Lagrangian heuristic, and variable splitting, are examined.
250 citations
TL;DR: In this paper, the authors present a heuristic approach to the multilevel generalized assignment problem (MGAP) which consists of a novel application of tabu search (TS), which employs neighborhoods defined by ejection chains, that produce moves of greater power without significantly increasing the computational effort.
Book•
26 Oct 1995
TL;DR: The discussion is supported by extensive citation of the literature and by descriptions of a few of the most successful heuristic (problem-solving) programs constructed to date.
Abstract: The problems of heuristic programming-of making computers solve really difficult problems-are divided into five main areas: Search, Pattern-Recognition, Learning, Planning, and Induction. A computer can do, in a sense, only what it is told to do. But even when we do not know how to solve a certain problem, we may program a machine (computer) to Search through some large space of solution attempts. Unfortunately, this usually leads to an enormously inefficient process. With Pattern-Recognition techniques, efficiency can often be improved, by restricting the application of the machine's methods to appropriate problems. Pattern-Recognition, together with Learning, can be used to exploit generalizations based on accumulated experience, further reducing search. By analyzing the situation, using Planning methods, we may obtain a fundamental improvement by replacing the given search with a much smaller, more appropriate exploration. To manage broad classes of problems, machines will need to construct models of their environments, using some scheme for Induction. Wherever appropriate, the discussion is supported by extensive citation of the literature and by descriptions of a few of the most successful heuristic (problem-solving) programs constructed to date.
01 Jan 1995
TL;DR: This paper reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step, and shows strong relation between R.ELIEF’s estimates and impurity functions, that are usually used for heuristic guidance of inductive learning algorithms.
Abstract: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies between then Greedy search prevents current inductive machine learning algorithms to detect significant dependencies between the attributes Recently, Kira and Rendell developed the RELIEF algorithm for estimating the quality of attributes that is able to detect dependencies between attributes We show strong relation between RELIEF’s estimates and impurity functions, that are usually used for heuristic guidance of inductive learning algorithms We propose to use RELIEFF, an extended version of RELIEF, instead of myopic impurity functions We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step The algorithm is tested on several artificial and several real world problems Results show the advantage of the presented approach to inductive learning and open a wide range of possibilities for using RELIEFF
TL;DR: In this paper, the authors examined whether there is a substantial additional payoff to be derived from using mathematical optimization techniques to globally define a set of mini-clusters and presented a new approximate method to mini clustering that involves solving a multi-vehicle pick-up and delivery problem with time windows by column generation.
Abstract: This paper examines whether there is a substantial additional payoff to be derived from using mathematical optimization techniques to globally define a set of mini-clusters. Specifically, we present a new approximate method to mini-clustering that involves solving a multi-vehicle pick-up and delivery problem with time windows by column generation. To solve this problem we have enhanced an existing optimal algorithm in several ways. First, we present an original network design based on lists of neighboring transportation requests. Second, we have developed a specialized initialization procedure which reduces the processing time by nearly 40%. Third, the algorithm was easily generalized to multi-dimensional capacity. Finally, we have also developed a heuristic to reduce the size of the network, while incurring only small losses in solution quality. This allows the application of our approach to much larger problems. To be able to compare the results of optimization-based and local heuristic mini-clustering,...
Proceedings Article•
20 Aug 1995TL;DR: This paper describes SEM, a System for Enumerating finite Models of first-order many-sorted theories, and shows that general purpose finite model generators are indeed useful in many applications.
Abstract: Model generation can be regarded as a special case of the Constraint Satisfaction Problem (CSP). It has many applications in AI, computer science and mathematics. In this paper, we describe SEM, a System for Enumerating finite Models of first-order many-sorted theories. To the best of our knowledge, SEM outperforms any other finite model generation system on many test problems. The high performance of SEM relies on the following two techniques: (a) an efficient implementation of constraint propagation which requires little dynamic allocation of storage; (b) a powerful heuristic which eliminates many isomorphic partial models during the search. We will present the basic algorithm of SEM along with these two techniques. Our experimental results show that general purpose finite model generators are indeed useful in many applications.
TL;DR: Three heuristic solution approaches to operational forest planning problems are presented based on Interchange, Simulated Annealing and Tabu search and indicate that these approaches provide near optimal solutions in relatively short amounts of computer time.
Abstract: Operational forest planning problems are typically very difficult problems to solve due to problem size and constraint structure. This paper presents three heuristic solution approaches to operational forest planning problems. We develop solution procedures based on Interchange, Simulated Annealing and Tabu search. These approaches represent new and unique solution strategies to this problem. Results are provided for applications to two actual forest planning problems and indicate that these approaches provide near optimal solutions in relatively short amounts of computer time.
TL;DR: A new technique is proposed, which incorporates the idea of simulated annealing into the practice of simulated evolution, in place of arbitrary heuristics, called GESA, which is used primarily for combinatorial optimization.
Abstract: Feasible approaches to the task of solving NP-complete problems usually entails the incorporation of heuristic procedures so as to increase the efficiency of the methods used. We propose a new technique, which incorporates the idea of simulated annealing into the practice of simulated evolution, in place of arbitrary heuristics. The proposed technique is called guided evolutionary simulated annealing (GESA). We report on the use of GESA approach primarily for combinatorial optimization. In addition, we report the case of function optimization, treating the task as a search problem. The traveling salesman problem is taken as a benchmark problem in the first case. Simulation results are reported. The results show that the GESA approach can discover a very good near optimum solution after examining an extremely small fraction of possible solutions. A very complicated function with many local minima is used in the second case. The results in both cases indicate that the GESA technique is a practicable method which yields consistent and good near optimal solutions, superior to simulated evolution. >
TL;DR: The authors formulate the problem exactly as an integer programming problem and propose a heuristic solution for this problem and show that it performs extremely well.
Abstract: Considers a problem of network design of personal communication services (PCS). The problem is to assign cells to the switches of a PCS network in an optimum manner. The authors consider two types of costs. One is the cost of handoffs between cells. The other is the cost of cabling (or trunking) between a cell site and its associated switch. The problem is constrained by the call volume that each switch can handle. The authors formulate the problem exactly as an integer programming problem. They also propose a heuristic solution for this problem and show that it performs extremely well. >
TL;DR: In this article, the problem of fitting pipes of different diameters into a shipping container is formulated as a nonlinear mixed integer programming problem and a number of heuristic procedures for solving this problem are developed.
20 Nov 1995
TL;DR: This dissertation developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem and found that performance improved as additional subpopulations were added to the computation.
Abstract: In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problem--a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
TL;DR: An interactive tool that has been built to implement techniques for generating test cases from formal specifications written in TRIO, a language that extends classical temporal logic to deal explicitly with time measures, based on interpretation algorithms of the TRIO language.
Abstract: We address the problem of automated derivation of functional test cases for real-time systems, by introducing techniques for generating test cases from formal specifications written in TRIO, a language that extends classical temporal logic to deal explicitly with time measures. We describe an interactive tool that has been built to implement these techniques, based on interpretation algorithms of the TRIO language. Several heuristic criteria are suggested to reduce drastically the size of the test cases that are generated. Experience in the use of the tool on real-life cases is reported.
TL;DR: In this article, a heuristic preference relation is developed as the basis for the heuristic so that only the potential job interchanges are checked for possible improvement with respect to these two objectives.
TL;DR: The authors introduce the methodology behind a novel hybrid neural/fuzzy system which merges the neural network and fuzzy logic technologies to solve fault detection problems.
Abstract: The use of electric motors in industry is extensive. These motors are exposed to a wide variety of environments and conditions which age the motor and make it subject to incipient faults. These incipient faults, if left undetected, contribute to the degradation and eventual failure of the motors. Artificial neural networks have been proposed and have demonstrated the capability of solving the motor monitoring and fault detection problem using an inexpensive, reliable, and noninvasive procedure. However, the major drawback of conventional artificial neural network fault detection is the inherent black box approach that can provide the correct solution, but does not provide heuristic interpretation of the solution. Engineers prefer accurate fault detection as well as the heuristic knowledge behind the fault detection process. Fuzzy logic is a technology that can easily provide heuristic reasoning while being difficult to provide exact solutions. The authors introduce the methodology behind a novel hybrid neural/fuzzy system which merges the neural network and fuzzy logic technologies to solve fault detection problems. They also discuss a training procedure for this neural/fuzzy fault detection system. This procedure is used to determine the correct solutions while providing qualitative, heuristic knowledge about the solutions. >
TL;DR: A branch-and-bound algorithm to solve the nonlinear resource allocation problem, defined as the minimization of a convex function over one convex constraint and bounded integer variables, is developed and modified to solve nonconvex problems so that a concave objective function can be handled.
Abstract: In this paper we study the nonlinear resource allocation problem, defined as the minimization of a convex function over one convex constraint and bounded integer variables. This problem is encountered in a variety of applications, including capacity planning in manufacturing and computer networks, production planning, capital budgeting, and stratified sampling. Despite its importance to these and other applications, the nonlinear resource allocation problem has received little attention in the literature. Therefore, we develop a branch-and-bound algorithm to solve this class of problems. First we present a general framework for solving the continuous-variable problem. Then we use this framework as the basis for our branch-and-bound method. We also develop reoptimization procedures and a heuristic that significantly improve the performance of the branch-and-bound algorithm. In addition, we show how the algorithm can be modified to solve nonconvex problems so that a concave objective function can be handled...
TL;DR: The task of training subsymbolic systems is considered as a combinatorial optimization problem and solved with the heuristic scheme of the reactive tabu search (RTS), which is applicable to nondifferentiable functions, is robust with respect to the random initialization, and effective in continuing the search after local minima.
Abstract: In this paper the task of training subsymbolic systems is considered as a combinatorial optimization problem and solved with the heuristic scheme of the reactive tabu search (RTS). An iterative optimization process based on a "modified local search" component is complemented with a meta-strategy to realize a discrete dynamical system that discourages limit cycles and the confinement of the search trajectory in a limited portion of the search space. The possible cycles are discouraged by prohibiting (i.e., making tabu) the execution of moves that reverse the ones applied in the most recent part of the search. The prohibition period is adapted in an automated way. The confinement is avoided and a proper exploration is obtained by activating a diversification strategy when too many configurations are repeated excessively often. The RTS method is applicable to nondifferentiable functions, is robust with respect to the random initialization, and effective in continuing the search after local minima. Three tests of the technique on feedforward and feedback systems are presented. >
TL;DR: This paper reviews the existing heuristic solution procedures, then presents two new algorithms to solve the rural postman problem near-optimally, and shows that the proposed new algorithms significantly outperformed the existing solution procedures.
01 Jun 1995
TL;DR: This paper proves that for the case of a single address register the decision problem is NP-complete and generalizes the problem to multiple address registers, and presents a formulation of the problem of optimal storage assignment such that explicit instructions for address arithmetic are minimized.
Abstract: DSP architectures typically provide indirect addressing modes with auto-increment and decrement. In addition, indexing mode is not available, and there are usually few, if any, general-purpose registers. Hence, it is necessary to use address registers and perform address arithmetic to access automatic variables. Subsuming the address arithmetic into auto-increment and auto-decrement modes improves the size of the generated code.In this paper we present a formulation of the problem of optimal storage assignment such that explicit instructions for address arithmetic are minimized. We prove that for the case of a single address register the decision problem is NP-complete. We then generalize the problem to multiple address registers. For both cases heuristic algorithms are given. Our experimental results indicate an improvement of 3.
27 Sep 1995
TL;DR: It is shown that finding the optimal removal set is an NP-complete problem, and therefore gives rise to heuristic algorithms, and the power control with removals combined algorithm emerges as the best approach with respect to both criteria.
Abstract: In this paper we study the mobile removal problem in a cellular PCS network where transmitter powers are constrained and controlled by a distributed constrained power control (DCPC) algorithm. Due to transmitter mobility and random signal propagation, there are system states where not all transmitters can be supported, even under the optimal power control. Thus, some of them should be removed. It can be shown that finding the optimal removal set is an NP-complete problem, and therefore gives rise to heuristic algorithms. In this paper we study and compare among three classes of transmitter removal algorithms, one-by-one removals, multiple removals and power control with removals combined. All removal algorithms are compared with respect to their outage probabilities and their time to convergence to a steady state. The power control with removals combined algorithm emerges as the best approach with respect to both criteria.
TL;DR: The results of the computational experiments show that simulated annealing is a suitable approach for solving this very difficult combinatorial optimization problem, in the sense that it provides feasible and low-cost solutions within reasonable CPU times.
TL;DR: This paper is a comparative study of order batching algorithms composed of four seed selection rules and four order addition rules for orderbatching problems and indicates that the economic convex hull algorithm has the best performance.
Abstract: In a man-on-board storage and retrieval system, orders are combined into batches and each batch is processed in a tour of the storage/retrieval (S/R) machine. Batching policy determines the way to combine orders to form batches. Since batching is an NP-hard problem, heuristic algorithms have been proposed. The major steps of these algorithms include seed selection and addition of orders to a batch. This paper is a comparative study of order batching algorithms composed of four seed selection rules and four order addition rules for order batching problems. Together, they constitute 16 algorithms. In addition, another method, namely the SL (small and large) algorithm, is also considered. The performances of these algorithms are compared along the three dimensions of shape factor, capacity of the S/R machine, and storage assignment policy. The results indicate that the economic convex hull algorithm has the best performance.