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Generalized Traveling Salesman Problem Reduction Algorithms
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"... The generalized traveling salesman problem (GTSP) is an extension of the wellknown traveling salesman problem. In GTSP, we are given a partition of cities into groups and we are required to find a minimum length tour that includes exactly one city from each group. The aim of this paper is to presen ..."
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The generalized traveling salesman problem (GTSP) is an extension of the wellknown traveling salesman problem. In GTSP, we are given a partition of cities into groups and we are required to find a minimum length tour that includes exactly one city from each group. The aim of this paper is to present a problem reduction algorithm that deletes redundant vertices and edges, preserving the value of the optimal solution. The algorithm’s running time is O(N 3) in the worst case, but it is significantly faster in practice. The algorithm has reduced the problem size by 15–20 % on average in our experiments and this has decreased the solution time by approximately 45 % for each of the three solvers considered. 1
Evolutionary Nonlinear Great Deluge for University Course Timetabling
 In proceeding of 2009 internation conference on hybrid artificial intelligent HAIS09, LNAI 5572, 2009
"... The aim of this paper is to extend our nonlinear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolut ..."
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The aim of this paper is to extend our nonlinear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a nonlinear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steadystate evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between nonlinear great deluge and evolutionary operators in solving university course timetabling problems.
A New Approach to Population Sizing for Memetic Algorithms: A Case Study for the Multidimensional Assignment Problem
"... Memetic Algorithms are known to be a powerful technique in solving hard optimization problems. To design a memetic algorithm one needs to make a host of decisions; selecting a population size is one of the most important among them. Most algorithms in the literature fix the population size to a cert ..."
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Memetic Algorithms are known to be a powerful technique in solving hard optimization problems. To design a memetic algorithm one needs to make a host of decisions; selecting a population size is one of the most important among them. Most algorithms in the literature fix the population size to a certain constant value. This reduces the algorithm’s quality since the optimal population size varies for different instances, local search procedures and running times. In this paper we propose an adjustable population size. It is calculated as a function of the running time of the whole algorithm and the average running time of the local search for the given instance. Note that in many applications the running time of a heuristic should be limited and therefore we use this limit as a parameter of the algorithm. The average running time of the local search procedure is obtained during the algorithm’s run. Some coefficients which are independent with respect to the instance or the local search are to be tuned before the algorithm run; we provide a procedure to find these coefficients. The proposed approach was used to develop a memetic algorithm for the Multidimensional Assignment Problem (MAP or sAP in the case of s dimensions) which is an extension of the wellknown assignment problem. MAP is NPhard and has a host of applications. We show that using adjustable population size makes the algorithm flexible to perform well for instances of very different sizes and types and for different running times and local searches. This allows us to select the most efficient local search for every instance type. The results of computational experiments for several instance families and sizes prove that the proposed algorithm performs efficiently for a wide range of the running times and clearly outperforms the stateofthe art 3AP memetic algorithm being given the same time.
Memetic Algorithms for Crossdomain Heuristic Search
"... Abstract—Hyperheuristic Flexible Framework (HyFlex) is an interface designed to enable the development, testing and comparison of iterative generalpurpose heuristic search algorithms, particularly selection hyperheuristics. A selection hyperheuristic is a high level methodology that coordinates ..."
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Abstract—Hyperheuristic Flexible Framework (HyFlex) is an interface designed to enable the development, testing and comparison of iterative generalpurpose heuristic search algorithms, particularly selection hyperheuristics. A selection hyperheuristic is a high level methodology that coordinates the interaction of a fixed set of low level heuristics (operators) during the search process. The Java implementation of HyFlex along with different problem domains was recently used in a competition, referred to as Crossdomain Heuristic Search Challenge (CHeSC2011). CHeSC2011 sought for the best selection hyperheuristic with the best median performance over a set of instances from six different problem domains. Each problem domain implementation contained four different types of operators, namely mutation, ruinrecreate, hill climbing and crossover. CHeSC2011 including the competing hyperheuristic methods currently serves as a benchmark for hyperheuristic research. Considering the type of the operators implemented under the HyFlex framework, CHeSC2011 could also be used as a benchmark to empirically compare the performance of appropriate variants of the evolutionary computation methods across a variety of problem domains for discrete optimisation. In this study, we investigate the performance and generality level of generic steadystate and transgenerational memetic algorithms which hybridize genetic algorithms with hill climbing across six problem domains of the CHeSC2011 benchmark. I.
Application of Improved Ant Colony Algorithm in Solving TSP
"... Using ant colony algorithm to solve TSP (traveling salesman problem) has some disadvantages as easily plunging into local minimum, slow convergence speed and so on. In order to find the optimal path accurately and rapidly, an improved ant colony algorithm is proposed. Experimental results show that ..."
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Using ant colony algorithm to solve TSP (traveling salesman problem) has some disadvantages as easily plunging into local minimum, slow convergence speed and so on. In order to find the optimal path accurately and rapidly, an improved ant colony algorithm is proposed. Experimental results show that the improved ant colony algorithm has better effectiveness for TSP problems solutions.
Languages and Systems Area
"... Abstract. This work analyzes many different models and algorithms in the literature for the optimization of routes and frequencies of buses, necessary in the framework of the support tools development to take decisions for the collective urban public transportation systems design. The problem is NP ..."
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Abstract. This work analyzes many different models and algorithms in the literature for the optimization of routes and frequencies of buses, necessary in the framework of the support tools development to take decisions for the collective urban public transportation systems design. The problem is NPhard, for which diverse heuristic procedures to resolve it have been proposed in the literature. The methods that pretend to be more applicable are those that permit interactivity. The main purpose of this work is to propose an efficient method for optimizing bus routes and their frequences considering heterogeneous vehicles, so that the final user obtains a feasible solution in a reasonable computation time. The optimization method proposed in this paper take into account a multiobjective function under diverse restrictions and an optimizer at two levels using two metaheuristic algorithms. This work presents a model based on two Genetic Algorithms.
Repairing wireless sensor network connectivity with mobility and
"... hopcount constraints ..."
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A Hybrid Heuristic Approach for Solving the Generalized Traveling Salesman Problem
"... The generalized traveling salesman problem (GTSP) is an NPhard problem that extends the classical traveling salesman problem by partitioning the nodes into clusters and looking for a minimum Hamiltonian tour visiting exactly one node from each cluster. In this paper, we combine the consultantguided ..."
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The generalized traveling salesman problem (GTSP) is an NPhard problem that extends the classical traveling salesman problem by partitioning the nodes into clusters and looking for a minimum Hamiltonian tour visiting exactly one node from each cluster. In this paper, we combine the consultantguided search technique with a localglobal approach in order to solve efficiently the generalized traveling salesman problem. We use candidate lists in order to reduce the search space and we introduce efficient variants of 2opt and 3opt local search in order to improve the solutions. The resulting algorithm is applied to Euclidean GTSP instances derived from the TSPLIB library. The experimental results show that our algorithm is able to compete with the best existing algorithms in terms of solution quality and running time.
Article Dubins Traveling Salesman Problem with Neighborhoods: A GraphBased Approach
"... algorithms ..."
Neighborhood Search for the Bounded Diameter Minimum Spaning Tree
"... Many optimization problems including the network design problem of finding the bounded diameter minimum spanning tree are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic algorithms that can find solution close to the optimal one within a ..."
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Many optimization problems including the network design problem of finding the bounded diameter minimum spanning tree are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic algorithms that can find solution close to the optimal one within a reasonable amount of time. Neighborhood search algorithms are a wide class of algorithms where at each iteration an improved solution is found by searching the “neighborhood ” of the current solution. A critical issue in the design of a neighborhood search algorithm is the choice of the neighborhood structure. Literature reveals that the larger the neighborhood, the better is the quality of the locally optimal solutions and the greater is the accuracy of the final solution obtained. At the same time, the larger the neighborhood search, the longer it takes to search the neighborhood for optimum. For this reason, an efficient search strategy is required to produce an effective heuristic in large neighborhoods. This paper focus on two known neighborhood structures for the BDMST problem. Both types of neighborhoods are large in the sense that they contain exponentially large number of candidate solutions. A novel intelligent neighborhood search technique (INST) is introduced and compared with the previously published local search techniques. Keywords Bounded Diameter Minimum spanning tree, Local search, Heuristics. I.