Showing papers on "Local search (optimization) published in 2003"

Handbook Of Metaheuristics

Abstract: List of Contributing Authors. Preface. 1. Scatter Search and Path Relinking: Advances and Applications F. Glover, et al. 2. An Introduction to Tabu Search M. Grenreau. 3. Genetic Algorithms C. Reeves. 4. Genetic Programming 5. A Gentle Introduction to Memetic Algorithms P. Moscato, C. Cotta. 6. Variable Neighborhood Search P. Hansen, N. Mladenovic. 7. Guided Local Search C. Voudouris, E. Tsang. 8. Greedy Randomized Adaptive Search Procedures M. Resende, C. Ribeiro. 9. The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances M. Doringo, T. Stutzle. 10. The Theory and Practice of Simulated Annealing D. Henderson, et al. 11. Iterated Local Search H. Lourenco, et al. 12. Multi-Start Methods R. Marti 13. Local Search and Constraint Programming F. Focacci, et al. 14. Constraint Satisfaction E. Freuder, M. Wallace. 15. Artificial Neural Networks for Combinatorial Optimization J.-Y. Potvin, K. Smith. 16. Hyper-Heuristics: An Emerging Direction in Modern Search Technology E. Burke, et al. 17. Parallel Strategies for Meta-Heuristics T.G. Crainic, M. Toulouse. 18. Metaheuristic Class Libraries A. Fink, et al. 19. Asynchronous Teams S. Talukdar, et al. Index.

Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm

Abstract: Shuffled Frog Leaping Algorithm (SFLA) is a meta-heuristic for solving discrete optimization problems. Here it is applied to determine optimal discrete pipe sizes for new pipe networks and for network expansions. SFLA is a population based, cooperative search metaphor inspired by natural memetics. The algorithm uses memetic evolution in the form of infection of ideas from one individual to another in a local search. The local search is similar in concept to particle swarm optimization. A shuffling strategy allows for the exchange of information between local searches to move toward a global optimum. This paper summarizes the development of SFLANET, a computer model that links SFLA and the hydraulic simulation software EPANET and its library functions. Application of SFLANET to literature network design problems is then described. Although the algorithm is in its initial stages of development, promising results were obtained.

Iterated local search

Abstract: This is a survey of "Iterated Local Search", a general purpose metaheuristic for finding good solutions of combinatorial optimization problems. It is based on building a sequence of (locally optimal) solutions by: (1) perturbing the current solution; (2) applying local search to that modified solution. At a high level, the method is simple, yet it allows for a detailed use of problem-specific properties. After giving a general framework, we cover the uses of Iterated Local Search on a number of well studied problems.

A Trigonometric Mutation Operation to Differential Evolution

Abstract: Previous studies have shown that differential evolution is an efficient, effective and robust evolutionary optimization method. However, the convergence rate of differential evolution in optimizing a computationally expensive objective function still does not meet all our requirements, and attempting to speed up DE is considered necessary. In this paper, a new local search operation, trigonometric mutation, is proposed and embedded into the differential evolution algorithm. This modification enables the algorithm to get a better trade-off between the convergence rate and the robustness. Thus it can be possible to increase the convergence velocity of the differential evolution algorithm and thereby obtain an acceptable solution with a lower number of objective function evaluations. Such an improvement can be advantageous in many real-world problems where the evaluation of a candidate solution is a computationally expensive operation and consequently finding the global optimum or a good sub-optimal solution with the original differential evolution algorithm is too time-consuming, or even impossible within the time available. In this article, the mechanism of the trigonometric mutation operation is presented and analyzed. The modified differential evolution algorithm is demonstrated in cases of two well-known test functions, and is further examined with two practical training problems of neural networks. The obtained numerical simulation results are providing empirical evidences on the efficiency and effectiveness of the proposed modified differential evolution algorithm.

The Theory and Practice of Simulated Annealing

Abstract: Simulated annealing is a popular local search meta-heuristic used to address discrete and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing is that it provides a means to escape local optima by allowing hill-climbing moves (i.e., moves which worsen the objective function value) in hopes of finding a global optimum. A brief history of simulated annealing is presented, including a review of its application to discrete and continuous optimization problems. Convergence theory for simulated annealing is reviewed, as well as recent advances in the analysis of finite time performance. Other local search algorithms are discussed in terms of their relationship to simulated annealing. The chapter also presents practical guidelines for the implementation of simulated annealing in terms of cooling schedules, neighborhood functions, and appropriate applications.

Fixed-outline floorplanning: enabling hierarchical design

Abstract: Classical floorplanning minimizes a linear combination of area and wirelength. When simulated annealing is used, e.g., with the sequence pair representation, the typical choice of moves is fairly straightforward. In this paper, we study the fixed-outline floorplan formulation that is more relevant to hierarchical design style and is justified for very large ASICs and SoCs. We empirically show that instances of the fixed-outline floorplan problem are significantly harder than related instances of classical floorplan problems. We suggest new objective functions to drive simulated annealing and new types of moves that better guide local search in the new context. Wirelength improvements and optimization of aspect ratios of soft blocks are explicitly addressed by these techniques. Our proposed moves are based on the notion of floorplan slack. The proposed slack computation can be implemented with all existing algorithms to evaluate sequence pairs, of which we use the simplest, yet semantically indistinguishable from the fastest reported . A similar slack computation is possible with many other floorplan representations. In all cases the computation time approximately doubles. Our empirical evaluation is based on a new floorplanner implementation Parquet-1 that can operate in both outline-free and fixed-outline modes. We use Parquet-1 to floorplan a design, with approximately 32000 cells, in 37 min using a top-down, hierarchical paradigm.

Planning through stochastic local search and temporal action graphs in LPG

Abstract: We present some techniques for planning in domains specified with the recent standard language PDDL2.1, supporting "durative actions" and numerical quantities. These techiques are implemented in LPG, a domain-independent planner that took part in the 3rd International Planning Competition (IPC). LPG is an incremental, any time system producing multi-criteria quality plans. The core of the system is based on a stochastic local search method and on a graph-based representation called "Temporal Action Graphs" (TA-graphs). This paper focuses on temporal planning, introducing TA-graphs and proposing some techniques to guide the search in LPG using this representation. The experimental results of the 3rd IPC, as well as further results presented in this paper, show that our techniques can be very effective. Often LPG outperforms all other fully-automated planners of the 3rd IPC in terms of speed to derive a solution, or quality of the solutions that can be produced.

A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows

Abstract: The purpose of this paper is to present a new deterministic metaheuristic based on a modification of the variable neighborhood search of Mladenovic and Hansen (1997) for solving the vehicle-routing problem with time windows. Results are reported for the standard 100, 200, and 400 customer data sets by Solomon (1987) and Gehring and Homberger (1999), and two real-life problems by Russell (1995). The findings indicate that the proposed procedure outperforms other recent local searches and metaheuristics. In addition, four new best-known solutions were obtained. The proposed procedure is based on a new four-phase approach. In this approach an initial solution is first created using new route-construction heuristics followed by a route-elimination procedure to improve the solutions regarding the number of vehicles. In the third phase the solutions are improved in terms of total traveled distance using four new local-search procedures proposed in this paper. Finally, in phase four, the best solution obtained is improved by modifying the objective function to escape from a local minimum.

An Introduction to Tabu Search

Abstract: This chapter presents the fundamental concepts of Tabu Search (TS) in a tutorial fashion. Special emphasis is put on showing the relationships with classical Local Search methods and on the basic elements of any TS heuristic, namely, the definition of the search space, the neighborhood structure, and the search memory. Other sections cover other important concepts such as search intensification and diversification and provide references to significant work on TS. Recent advances in TS are also briefly discussed.

Iterated Local Search

Abstract: This is a survey of "Iterated Local Search", a general purpose metaheuristic for finding good solutions of combinatorial optimization problems. It is based on building a sequence of (locally optimal) solutions by: (1) perturbing the current solution; (2) applying local search to that modified solution. At a high level, the method is simple, yet it allows for a detailed use of problem-specific properties. After giving a general framework, we cover the uses of Iterated Local Search on a number of well studied problems.

Ant algorithms for the university course timetabling problem with regard to the state-of-the-art

Abstract: Two ant algorithms solving a simplified version of a typical university course timetabling problem are presented -Ant Colony System and MAX-MIN Ant System The algorithms are tested over a set of instances from three classes of the problem Results are compared with recent results obtained with several metaheuristics using the same local search routine (or neighborhood definition), and a reference random restart local search algorithm Further, both ant algorithms are compared on an additional set of instances Conclusions are drawn about the performance of ant algorithms on timetabling problems in comparison to other metaheuristics Also the design, implementation, and parameters of ant algorithms solving the university course timetabling problem are discussed It is shown that the particular implementation of an ant algorithm has significant influence on the observed algorithm performance

A multiple hill climbing approach to software module clustering

Abstract: Automated software module clustering is important for maintenance of legacy systems written in a 'monolithic format' with inadequate module boundaries. Even where systems were originally designed with suitable module boundaries, structure tends to degrade as the system evolves, making re-modularization worthwhile. This paper focuses upon search-based approaches to the automated module clustering problem, where hitherto, the local search approach of hill climbing has been found to be most successful. In the paper we show that results from a set of multiple hill climbs can be combined to locate good 'building blocks' for subsequent searches. Building blocks are formed by identifying the common features in a selection of best hill climbs. This process reduces the search space, while simultaneously 'hard wiring' parts of the solution. The paper reports the results of an empirical study that show that the multiple hill climbing approach does indeed guide the search to higher peaks in subsequent executions. The paper also investigates the relationship between the improved results and the system size.

Solving Project Scheduling Problems by Minimum Cut Computations

Abstract: In project scheduling, a set of precedence-constrained jobs has to be scheduled so as to minimize a given objective. In resource-constrained project scheduling, the jobs additionally compete for scarce resources. Due to its universality, the latter problem has a variety of applications in manufacturing, production planning, project management, and elsewhere. It is one of the most intractable problems in operations research, and has therefore become a popular playground for the latest optimization techniques, including virtually all local search paradigms. We show that a somewhat more classical mathematical programming approach leads to both competitive feasible solutions and strong lower bounds, within reasonable computation times. The basic ingredients of our approach are the Lagrangian relaxation of a time-indexed integer programming formulation and relaxation-based list scheduling, enriched with a useful idea from recent approximation algorithms for machine scheduling problems. The efficiency of the algorithm results from the insight that the relaxed problem can be solved by computing a minimum cut in an appropriately defined directed graph. Our computational study covers different types of resource-constrained project scheduling problems, based on several notoriously hard test sets, including practical problem instances from chemical production planning.

Solving the p-center problem with tabu search and variable neighborhood search

Abstract: The p-Center problem consists of locating p facilities and assigning clients to them in order to minimize the maximum distance between a client and the facility to which he or she is allocated. In this paper, we present a basic Variable Neighborhood Search and two Tabu Search heuristics for the p-Center problem without the triangle inequality. Both proposed methods use the 1-interchange (or vertex substitution) neighborhood structure. We show how this neighborhood can be used even more efficiently than for solving the p-Median problem. Multistart 1-interchange, Variable Neighborhood Search, Tabu Search, and a few early heuristics are compared on small- and large-scale test problems from the literature. © 2003 Wiley Periodicals, Inc.

Optimization of cardinality constrained portfolios with a hybrid local search algorithm

Abstract: One of the main advantages of portfolios over single assets is that risk can be diversified without necessarily reducing the expected return - provided "proper" assets are selected and they are assigned the "proper" weights. Since in practice investors tend to restrict themselves to a rather small number of different assets, the decision which securities to include is a crucial one that turns out to be NP-hard.

A complete and effective move set for simplified protein folding

Abstract: We present new lowest energy configurations for several large benchmark problems for the two-dimensional hydrophobic-hydrophilic model. We found these solutions with a generic implementation of tabu search using an apparently novel set of transformations that we call pull moves. Our experiments show that our algorithm can find these best solutions in 3 to 14 hours, on average. Pull moves appear quite effective and may also be useful for other local search algorithms for the problem. Additionally, we prove that pull moves are complete; that is, any pair of valid configurations are mutually reachable through a sequence of pull moves. Our implementation was developed with the Human-Guided Search (HuGS) middleware, which allows rapid development of interactive optimization systems.

Speeding up backpropagation using multiobjective evolutionary algorithms

Abstract: The use of backpropagation for training artificial neural networks (ANNs) is usually associated with a long training process. The user needs to experiment with a number of network architectures; with larger networks, more computational cost in terms of training time is required. The objective of this letter is to present an optimization algorithm, comprising a multiobjective evolutionary algorithm and a gradient-based local search. In the rest of the letter, this is referred to as the memetic Pareto artificial neural network algorithm for training ANNs. The evolutionary approach is used to train the network and simultaneously optimize its architecture. The result is a set of networks, with each network in the set attempting to optimize both the training error and the architecture. We also present a self-adaptive version with lower computational cost. We show empirically that the proposed method is capable of reducing the training time compared to gradient-based techniques.

Scatter Search and Path Relinking: Advances and Applications

Abstract: Scatter search (SS) is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, SS uses strategies for combining solution vectors that have proved effective in a variety of problem settings. Path relinking (PR) has been suggested as an approach to integrate intensification and diversification strategies in a search scheme. The approach may be viewed as an extreme (highly focused) instance of a strategy that seeks to incorporate attributes of high quality solutions, by creating inducements to favor these attributes in the moves selected. The goal of this paper is to examine SS and PR strategies that provide useful alternatives to more established search methods. We describe the features of SS and PR that set them apart from other evolutionary approaches, and that offer opportunities for creating increasingly more versatile and effective methods in the future. Specific applications are summarized to provide a clearer understanding of settings where the methods are being used.

A comparison of the performance of different metaheuristics on the timetabling problem

Abstract: The main goal of this paper is to attempt an unbiased comparison of the performance of straightforward implementations of five different metaheuristics on a university course timetabling problem In particular, the metaheuristics under consideration are Evolutionary Algorithms, Ant Colony Optimization, Iterated Local Search, Simulated Annealing, and Tabu Search To attempt fairness, the implementations of all the algorithms use a common solution representation, and a common neighbourhood structure or local search The results show that no metaheuristic is best on all the timetabling instances considered Moreover, even when instances are very similar, from the point of view of the instance generator, it is not possible to predict the best metaheuristic, even if some trends appear when focusing on particular instance classes These results underline the difficulty of finding the best metaheuristics even for very restricted classes of timetabling problem

Hierarchical BOA solves ising spin glasses and MAXSAT

Abstract: Theoretical and empirical evidence exists that the hierarchical Bayesian optimization algorithm (hBOA) can solve challenging hierarchical problems and anything easier. This paper applies hBOA to two important classes of real-world problems: Ising spin-glass systems and maximum satisfiability (MAXSAT). The paper shows how easy it is to apply hBOA to real-world optimization problems--in most cases hBOA can be applied without any prior problem analysis, it can acquire enough problem-specific knowledge automatically. The results indicate that hBOA is capable of solving enormously difficult problems that cannot be solved by other optimizers and still provide competitive or better performance than problem-specific approaches on other problems. The results thus confirm that hBOA is a practical, robust, and scalable technique for solving challenging real-world problems.

EasyLocal++: an object-oriented framework for the flexible design of local-search algorithms

Abstract: Local search is a paradigm for search and optimization problems, which has recently evidenced to be very effective for a large number of combinatorial problems. Despite the increasing interest of the research community in this subject, there is still a lack of a widely-accepted software tools for local search.We propose EASYLOCAL++, an object-oriented framework for the design and the analysis of local-search algorithms. The abstract classes that compose the framework specify and implement the invariant part of the algorithm and are meant to be specialized by concrete classes that supply the problem-dependent part. The framework provides the full control structures of the algorithms, and the user has only to write the problem-specific code. Furthermore, the framework comes with some tools that simplify the analysis of the algorithms.The architecture of EASYLOCAL++ provides a principled modularization for the solution of combinatorial problems by local search and helps the user by deriving a neat conceptual scheme of the application. It also supports the design of combinations of basic techniques and/or neighborhood structures.The framework has been tested in some applicative domains and has proved to be flexible enough in the implementation of algorithms for the solution of various scheduling problems.

A Two-Phase Local Search for the biobjective traveling salesman problem

Abstract: This article proposes the Two-Phase Local Search for finding a good approximate set of non-dominated solutions The two phases of this procedure are to (i) generate an initial solution by optimizing only one single objective, and then (ii) to start from this solution a search for non-dominated solutions exploiting a sequence of different formulations of the problem based on aggregations of the objectives This second phase is a single chain, using the local optimum obtained in the previous formulation as a starting solution to solve the next formulation Based on this basic idea, we propose some further improvements and report computational results on several instances of the biobjective TSP that show competitive results with state-of-the-art algorithms for this problem

Use of an Artificial Immune System for Job Shop Scheduling

Abstract: In this paper, we propose an algorithm based on an artificial immune system to solve job shop scheduling problems. The approach uses clonal selection, hypermutations and a library of antibodies to construct solutions. It also uses a local selection mechanism that tries to eliminate gaps between jobs in order to improve solutions produced by the search mechanism of the algorithm. The proposed approach is compared with respect to GRASP (an enumerative approach) in several test problems taken from the specialized literature. Our results indicate that the proposed algorithm is highly competitive, being able to produce better solutions than GRASP in several cases, at a fraction of its computational cost.

Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs

Abstract: Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.

Universal Facility Location

Abstract: In the Universal Facility Location problem we are given a set of demand points and a set of facilities. The goal is to assign the demands to facilities in such a way that the sum of service and facility costs is minimized. The service cost is proportional to the distance each unit of demand has to travel to its assigned facility, whereas the facility cost of each facility i depends on the amount of demand assigned to that facility and is given by a cost function f i (·). We present a (7.88 + e)-approximation algorithm for the Universal Facility Location problem based on local search, under the assumption that the cost functions f i are nondecreasing. The algorithm chooses local improvement steps by solving a knapsack-like subproblem using dynamic programming. This is the first constant-factor approximation algorithm for this problem. Our algorithm also slightly improves the best known approximation ratio for the capacitated facility location problem with non-uniform hard capacities.

Iterated robust tabu search for MAX-SAT

Abstract: MAX-SAT, the optimisation variant of the satisfiability problem in propositional logic, is an important and widely studied combinatorial optimisation problem with applications in AI and other areas of computing science. In this paper, we present a new stochastic local search (SLS) algorithm for MAX-SAT that combines Iterated Local Search and Tabu Search, two well-known SLS methods that have been successfully applied to many other combinatorial optimisation problems. The performance of our new algorithm exceeds that of current state-of-the-art MAX-SAT algorithms on various widely studied classes of unweighted and weighted MAX-SAT instances, particularly for Random-3-SAT instances with high variance clause weight distributions. We also report promising results for various classes of structured MAX-SAT instances.