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Opposition-based learning (OBL) is a recently proposed method, which is successfully used to accelerate the search process of some well-known techniques in soft computing, such as swarm and evolutionary algorithms, artificial neural networks, reinforcement learning, and fuzzy logic systems. Among these opposition-based algorithms, opposition-based differential evolution (ODE) is one of the most popular...
Firefly algorithm (FA) is a population-based stochastic algorithm, which is inspired by the behavior of the flashing of fireflies. Though some recent studies show that FA is effective on many optimization problems, its performance is greatly influenced by its control parameters. In this paper, a new FA called adaptive FA with alternative search (AFAas) is proposed to improve the performance of FA...
Communities play fundamental organizational and functional roles in various complex network systems. Community detection is an important challenge in network analysis. We approach community detection based on a Shared-Influence-Neighbor (SIN) similarity metric that measures the closeness of a pair of nodes in terms of their mutual influence and the common set of nodes they both influence. In this...
Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail...
Vehicle routing problem with time windows (VRPTW) is a well-known and complex combinatorial problem, which has received considerable attention in recent years. In this paper, we propose an improved genetic algorithm to solve the VRPTW problem. The proposed approach, called IGA, employs two novel genetic operators. To verify the performance of IGA, we test it on six famous benchmark problems. Simulation...
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