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A New Local Search Algorithm Providing High Quality Solutions to Vehicle Routing Problems
, 1997
"... This paper describes a new local search algorithm that provides very high quality solutions to vehicle routing problems. The method uses greedy local search, but avoids local minima by using a large neighbourhood based upon rescheduling selected customer visits using constraint programming technique ..."
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Cited by 25 (0 self)
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This paper describes a new local search algorithm that provides very high quality solutions to vehicle routing problems. The method uses greedy local search, but avoids local minima by using a large neighbourhood based upon rescheduling selected customer visits using constraint programming techniques. The move operator adopted is completely generic, in that virtually any side constraint can be efficiently incorporated into the search process. Computational results show that a naive implementation of the method produces results bettering the best produced by competing techniques using minimaescaping methods. 1 Introduction In recent years, the method of choice for solving vehicle routing problems has been to use a local search technique. These local search methods have been favoured since they quickly provide solutions to problems of practical size that have not been solved by exact methods. However, because local search techniques only make small changes to the solution, they can onl...
Iterated Local Search: Framework and Applications
"... The importance of high performance algorithms for tackling difficult optimization problems cannot be understated, and in many cases the most effective methods are metaheuristics. When designing a metaheuristic, simplicity should be favored, both conceptually and in practice. Naturally, it must also ..."
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Cited by 18 (1 self)
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The importance of high performance algorithms for tackling difficult optimization problems cannot be understated, and in many cases the most effective methods are metaheuristics. When designing a metaheuristic, simplicity should be favored, both conceptually and in practice. Naturally, it must also
A NEW VERTEX COLORING ALGORITHM BASED ON VARIABLE ACTIONSET LEARNING AUTOMATA
"... Abstract. In this paper, we propose a learning automatabased iterative algorithm for approximating a near optimal solution to the vertex coloring problem. Vertex coloring is a wellknown NPhard optimization problem in graph theory in which each vertex is assigned a color so that no two adjacent ve ..."
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Cited by 16 (7 self)
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Abstract. In this paper, we propose a learning automatabased iterative algorithm for approximating a near optimal solution to the vertex coloring problem. Vertex coloring is a wellknown NPhard optimization problem in graph theory in which each vertex is assigned a color so that no two adjacent vertices have the same color. Each iteration of the proposed algorithm is subdivided into several stages, and at each stage a subset of the uncolored non adjacent vertices are randomly selected and assigned the same color. This process continues until no more vertices remain uncolored. As the proposed algorithm proceeds, taking advantage of the learning automata the number of stages per iteration and so the required number of colors tends to the chromatic number of the graph since the number of vertices which are colored at each stage is maximized. To show the performance of the proposed algorithm we compare it with several existing vertex coloring algorithms in terms of the time and the number of colors required for coloring the graphs. The obtained results show the superiority of the proposed algorithm over the others.
Guided Local Search for the ThreeDimensional Bin Packing Problem
 INFORMS Journal on Computing
, 1999
"... The threedimensional bin packing problem is the problem of orthogonally packing a set of boxes into a minimum number of threedimensional bins. In this paper we present a heuristic algorithm based on Guided Local Search (GLS). Starting with an upper bound on the number of bins obtained by a gree ..."
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Cited by 16 (2 self)
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The threedimensional bin packing problem is the problem of orthogonally packing a set of boxes into a minimum number of threedimensional bins. In this paper we present a heuristic algorithm based on Guided Local Search (GLS). Starting with an upper bound on the number of bins obtained by a greedy heuristic, the presented algorithm iteratively decreases the number of bins, each time searching for a feasible packing of the boxes using GLS. The process terminates when a given time limit has been reached or the upper bound matches a precomputed lower bound. The algorithm can also be applied to twodimensional bin packing problems by having a constant depth for all boxes and bins. Computational experiments are reported for two and threedimensional instances with up to 200 boxes, and the results are compared with those obtained by heuristics and exact methods from the literature. 1 Introduction The threedimensional bin packing problem asks for an orthogonal packing of a set ...
Vehicle Routing and Job Shop Scheduling: What's the difference?
 Proc. of the 13th International Conference on Automated Planning and Scheduling
, 2003
"... Despite a number of similarities, vehicle routing problems and scheduling problems are typically solved with different techniques. In this paper, we undertake a systematic study of problem characteristics that differ between vehicle routing and scheduling problems in order to identify those that ..."
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Cited by 14 (2 self)
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Despite a number of similarities, vehicle routing problems and scheduling problems are typically solved with different techniques. In this paper, we undertake a systematic study of problem characteristics that differ between vehicle routing and scheduling problems in order to identify those that are important for the performance of typical vehicle routing and scheduling techniques. In particular, we find that the addition of temporal constraints among visits or the addition of tight vehicle specialization constraints significantly improves the performance of scheduling techniques relative to vehicle routing techniques.
Exact and Memetic Algorithms for Two Network Design Problems
, 2004
"... This thesis focuses on two combinatorial optimization problems (COPs) that belong to the class of NPhard network design problems: The first one, vertex biconnectivity augmentation (V2AUG), appears in the design of survivable communication or electricity networks. In this problem we search for the s ..."
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Cited by 14 (5 self)
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This thesis focuses on two combinatorial optimization problems (COPs) that belong to the class of NPhard network design problems: The first one, vertex biconnectivity augmentation (V2AUG), appears in the design of survivable communication or electricity networks. In this problem we search for the set of connections of minimal total cost which, when added to an existing network, makes it survivable against failures of any single node. The second problem, the prizecollecting Steiner tree problem (PCST), describes a natural tradeoff between maximizing the sum of profits over all selected customers and minimizing the implementation costs, e.g. when designing a fiber optic or a district heating network. The available techniques for COPs can roughly be classified into two main categories: exact and heuristic algorithms. Exact algorithms are guaranteed to find an optimal solution and to prove its optimality for every instance of a COP. Due to sometimes exponential running times or memory requirements of exact algorithms we sometimes sacrifice the guarantee of finding optimal solutions for the sake of getting good solutions in a limited time and therefore use heuristic algorithms. This thesis provides tools that can solve given network design problems of respectable size to provable optimality. For fairly large instances, these tools obtain suboptimal,
Function Optimization using Guided Local Search
, 1995
"... In this report, we examine the potential use of Guided Local Search (GLS) for function optimization. In order to apply GLS, the function to be minimized is augmented with a set of penalty terms that enable local search to escape from local minima. The function F6 is used to demonstrate the proposed ..."
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Cited by 14 (3 self)
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In this report, we examine the potential use of Guided Local Search (GLS) for function optimization. In order to apply GLS, the function to be minimized is augmented with a set of penalty terms that enable local search to escape from local minima. The function F6 is used to demonstrate the proposed technique. 1. Introduction In this report, we present preliminary findings on the potential use of Guided Local Search (GLS) for function optimization. GLS is a metaheuristic for guiding local search [3] to escape local minima and visit promising solutions. GLS has been used to tackle difficult combinatorial optimization problems [7,5] and derives itself from the GENET network for constraint satisfaction problems [6]. Function optimization can be seen as a combinatorial problem by encoding real variables as binary strings [2]. In the simple case of binary encoding, binary string values are converted to integers which then are scaled by the appropriate coefficient to give real values in the...
Guided genetic algorithm and its application to the radio link frequency allocation problem
 Proceedings of NATO symposium on Frequency Assignment, Sharing and Conservation in Systems (AEROSPACE), AGARD, RTOMP13, paper No. 14b
, 1998
"... Abstract. The Guided Genetic Algorithm (GGA) is a hybrid of Genetic Algorithm and Guided Local Search, a metaheuristic search algorithm. As the search progresses, GGA modies both the tness function and tness template of candidate solutions based on feedback from constraints. The tness template is ..."
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Cited by 13 (5 self)
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Abstract. The Guided Genetic Algorithm (GGA) is a hybrid of Genetic Algorithm and Guided Local Search, a metaheuristic search algorithm. As the search progresses, GGA modies both the tness function and tness template of candidate solutions based on feedback from constraints. The tness template is then used to bias crossover and mutation. The Radio Link Frequency Assignment Problem (RLFAP) is a class of problem that has practical relevance to both military and civil applications. In this paper, we show how GGA can be applied to the RLFAP. We focus on an abstraction of a real life military application that involves the assigning of frequencies to radio links. GGA was tested on a set of eleven benchmark problems provided by the French military. This set of problems has been studied intensively by a number of prominent groups in Europe. It covers a variety of needs in military applications, including the satisfaction of constraints, nding optimal solutions that satisfy all the constraints and optimization of some objective functions whenever no solution exist ("partial constraint satisfaction"). Not only do these benchmark problems vary in problem nature, they are reasonably large for military applications (up to 916 variables, and up to 5548 constraints). This makes them a serious challenge to the generality, reliability as well as eÆciency of algorithms. We show in this paper that GGA is capable of producing excellent results reliably in the whole set of benchmark problems.
Mapping Heavy Communication GridBased Workflows onto Grid Resources Within An SLA Context Using Metaheuristics
 International Journal of High Performance Computing and Application (IJHPCA
, 2007
"... Service Level Agreements (SLAs) is currently one of the major research topics in grid computing. Among many system components for the SLArelated grid jobs, the SLA mapping mechanism has received wide spread attention. It is responsible for assigning subjobs of a workflow to a variety of grid resou ..."
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Cited by 9 (1 self)
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Service Level Agreements (SLAs) is currently one of the major research topics in grid computing. Among many system components for the SLArelated grid jobs, the SLA mapping mechanism has received wide spread attention. It is responsible for assigning subjobs of a workflow to a variety of grid resources in a way that meets the user's deadline and costs as little as possible. With the distinguished workload and resource characteristics, mapping a heavy communication workflow within an SLA context gives rise to a complicated combinatorial optimization problem. This paper presents the application of various metaheuristics and suggests a possible approach to solve this problem. Performance measurements deliver evaluation results on the quality and efficiency of each method.
Graph Coloring Problem Based on Learning Automata
"... Abstract — The vertex coloring problem is a wellknown classical problem in graph theory in which a color is assigned to each vertex of the graph such that no two adjacent vertices have the same color. The minimum vertex coloring problem is known to be an NPhard problem in an arbitrary graph, and a ..."
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Cited by 8 (3 self)
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Abstract — The vertex coloring problem is a wellknown classical problem in graph theory in which a color is assigned to each vertex of the graph such that no two adjacent vertices have the same color. The minimum vertex coloring problem is known to be an NPhard problem in an arbitrary graph, and a host of approximation solutions are available. In this paper, four learning automatabased approximation algorithms are proposed for solving the minimum (vertex) coloring problem. It is shown that by a proper choice of the parameters of the algorithm, the probability of approximating the optimal solution is as close to unity as possible. The last proposed algorithm is compared with some wellknown coloring algorithms and the results show the efficiency of the proposed algorithm in terms of the color set size and running time of algorithm. Keywordscomponent; Graph coloring problem, vertex coloring problem, combinatorial optimization problem, learning automata I.