A New Local Search Heuristic for the Multidimensional Assignment Problem

2017 ◽  
pp. 183-202
Author(s):  
Sergio Luis Pérez Pérez ◽  
Carlos E. Valencia ◽  
Francisco Javier Zaragoza Martínez
Keyword(s):  
Local Search ◽  
Assignment Problem ◽  
Search Heuristic ◽  
Multidimensional Assignment Problem ◽  
Local Search Heuristic

scholarly journals Deanonymizing Social Networks Using Structural Information

2019 ◽  
Author(s):  
Ioannis Caragiannis ◽  
Evanthia Tsitsoka
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Search ◽  
Structural Information ◽  
Initial Solution ◽  
Bipartite Matching ◽  
Search Heuristic ◽  
Local Search Heuristic ◽  
Anonymous Network ◽  
Random Models

We study the following fundamental graph problem that models the important task of deanonymizing social networks. We are given a graph representing an eponymous social network and another graph, representing an anonymous social network, which has been produced by the original one after removing some of its nodes and adding some noise on the links. Our objective is to correctly associate as many nodes of the anonymous network as possible to their corresponding node in the eponymous network. We present two algorithms that attack the problem by exploiting only the structure of the two graphs. The first one exploits bipartite matching computations and is relatively fast. The second one is a local search heuristic which can use the outcome of our first algorithm as an initial solution and further improve it. We have applied our algorithms on inputs that have been produced by well-known random models for the generation of social networks as well as on inputs that use real social networks. Our algorithms can tolerate noise at the level of up to 10%. Interestingly, our results provide further evidence to which graph generation models are most suitable for modeling social networks and distinguish them from unrealistic ones.

scholarly journals A New Approach to Population Sizing for Memetic Algorithms: A Case Study for the Multidimensional Assignment Problem

2011 ◽  
Vol 19 (3) ◽  
pp. 345-371 ◽  
Author(s):  
Daniel Karapetyan ◽  
Gregory Gutin
Keyword(s):  
Local Search ◽  
Population Size ◽  
Assignment Problem ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Memetic Algorithms ◽  
Multidimensional Assignment Problem ◽  
Local Searches ◽  
Wide Range ◽  
Local Search Procedure

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 the population size is one of the most important among them. Most of the 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 runtimes. In this paper we propose an adjustable population size. It is calculated as a function of the runtime of the whole algorithm and the average runtime of the local search for the given instance. Note that in many applications the runtime of a heuristic should be limited and, therefore, we use this bound as a parameter of the algorithm. The average runtime of the local search procedure is measured during the algorithm's run. Some coefficients which are independent of the instance and the local search are to be tuned at the design time; we provide a procedure to find these coefficients. The proposed approach was used to develop a memetic algorithm for the multidimensional assignment problem (MAP). We show that our adjustable population size makes the algorithm flexible to perform efficiently for a wide range of running times and local searches and this does not require any additional tuning of the algorithm.

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