40,900 research outputs found

    Review On The Methods To Solve Combinatorial Optimization Problems Particularly:Quadratic Assignment Model

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    The quadratic assignment problem (QAP) is one of the fundamental combinatorial optimization problem (COPs) in the branch of optimization or operation research in mathematics,from the category of the Facilities Location Problems (FLPs).The quadratic assignment problem (QAP) be appropriate to the group of NP-hard issues and is measured as a challenging problem of the combinatorial optimization.QAP in Location Theory considers one of the problems of facilities tracing which the rate of locating a facility be determined by the spaces between facilities as well as the communication among the further facilities.QAP was presented in 1957 by Beckman and Koopmans as they were attempting to model a problem of facilities location.To survey the researcher’s works for QAP and applied,the mapped research landscape outlines literature into a logical classification and discovers this field basic characteristics represented on the motivation to use the quadratic assignment problem applied in hospital layout and campus planning.This survey achieved a concentrated each QAP article search in three key databases:Web of Science,Science Direct,and IEEE Xplore.Those databases are regarded extensive adequate in covering QAP and the methods utilized in solving QAP

    Comparative Performance of Tabu Search and Simulated Annealing Heuristics for the Quadratic Assignment Problem

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    For almost two decades the question of whether tabu search (TS) or simulated annealing (SA) performs better for the quadratic assignment problem has been unresolved. To answer this question satisfactorily, we compare performance at various values of targeted solution quality, running each heuristic at its optimal number of iterations for each target. We find that for a number of varied problem instances, SA performs better for higher quality targets while TS performs better for lower quality targets

    An Efficient Implementation of the Robust Tabu Search Heuristic for Sparse Quadratic Assignment Problems

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    We propose and develop an efficient implementation of the robust tabu search heuristic for sparse quadratic assignment problems. The traditional implementation of the heuristic applicable to all quadratic assignment problems is of O(N^2) complexity per iteration for problems of size N. Using multiple priority queues to determine the next best move instead of scanning all possible moves, and using adjacency lists to minimize the operations needed to determine the cost of moves, we reduce the asymptotic complexity per iteration to O(N log N ). For practical sized problems, the complexity is O(N)

    PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem

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    Multi-Objective Optimization Problems (MOPs) have attracted growing attention during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have been extensively used to address MOPs because are able to approximate a set of non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP which has been extensively studied, and used in several real-life applications. The mQAP is defined as having as input several flows between the facilities which generate multiple cost functions that must be optimized simultaneously. In this study, we propose PasMoQAP, a parallel asynchronous memetic algorithm to solve the Multi-Objective Quadratic Assignment Problem. PasMoQAP is based on an island model that structures the population by creating sub-populations. The memetic algorithm on each island individually evolve a reduced population of solutions, and they asynchronously cooperate by sending selected solutions to the neighboring islands. The experimental results show that our approach significatively outperforms all the island-based variants of the multi-objective evolutionary algorithm NSGA-II. We show that PasMoQAP is a suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.Comment: 8 pages, 3 figures, 2 tables. Accepted at Conference on Evolutionary Computation 2017 (CEC 2017

    Computational problems without computation

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    Problemen uit de discrete wiskunde lijken op het eerste gezicht vaak erg simpel. Ze kunnen meestal gemakkelijk en zonder gebruik te maken van wiskundige begrippen worden geformuleerd. Toch komt het vaak voor dat zo’n ogenschijnlijk eenvoudig probleem nog open is of dat er, zoals bij het handelsreizigersprobleem, wel een oplossing gegeven kan worden,maar alleen een die onbruikbaar is omdat de rekentijd bij grotere getallen te snel groeit. In dit artikel, gebaseerd op zijn voordracht op het NMC 2002, kijkt Gerhard Woeginger naar de tegenovergestelde situatie. Hij introduceert allerlei discrete\ud problemen die onoplosbaar lijken, maar waarvoor er een simpele oplossing bestaat
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