Efficient Metaheuristic Approaches for Exploration of Online Social Networks

Abstract

This study presents a novel approach in analyzing big data from social networks based on optimization techniques for efficient exploration of information flow within a network. Three mathematical models are proposed, which use similar assumptions on a social network and different objective functions reflecting different search goals. Since social networks usually involve a large number of users, solving the proposed models to optimality is out of reach for exact methods due to memory or time limits. Therefore, three metaheuristic methods are designed to solve problems of large-scaled dimensions: a robust Evolutionary Algorithm and two hybrid methods that represent a combination of Evolutionary Algorithm with Local Search and Tabu Search methods, respectively. The results of computational experiments indicate that the proposed metaheuristic methods are efficient in detecting trends and linking behavior within a social network, which is important for providing a support to decision-making activities in a limited amount of time.

Memetic algorithm for the balanced resource location problem with preferences

“…Particle swarm optimization method and evolutionary algorithms are often used as population-based approaches in the MA, while local search, tabu search, iterated local search, simulated annealing, may be used as improvement heuristic. This type of hybridization showed to be very successful for many optimization problems in the literature [5], [10], [16], [17], [21], [24], etc.…”

“…In the load balance model considered in this paper, user preferences ensure that customers are not necessarily assigned to their closest resources. Therefore, the second problem studied in [21] may be observed as a special case of the Balanced Resource Location Problem with Preferences considered in this study.…”

“…The goal of one of the proposed models is to ensure the load balance between the established control nodes, i.e., ensuring that located control nodes will be as equally loaded as possible, which will lead to minimizing the searching time. A combination of Evolutionary Algorithm with Local Search and Tabu Search methods is used to solve problem of large-scaled dimensions in [21]. Another application of load balance problems is in cloud computing.…”

“…Load balancing has also been found to provide an effective and scalable way of managing Big Data. In [21], three models have been proposed to provide simple, but efficient strategy of exploring the Big Data flow in a social network. The goal of one of the proposed models is to ensure the load balance between the established control nodes, i.e., ensuring that located control nodes will be as equally loaded as possible, which will lead to minimizing the searching time.…”

“…Some applications of load balance model in facility location is the design of telecommunication systems, for example, locating antennas for mobile phones, base stations or switching centers for telecommunication. Another application is in the field of territory design [6], when smaller geographic areas need to be grouped into larger geographic clusters, in respect to some planing criteria (political districting, solid waste collection, school districting, etc) Note that load balance models from [9], [23], [21] are useful in cases when customers naturally choose the closest or cheapest facility, such as supermarket, school, public transport station, base station, etc. In the load balance model considered in this paper, user preferences ensure that customers are not necessarily assigned to their closest resources.…”