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Solving Maximum Clique Problem in Stochastic Graphs Using Learning Automata
"... Abstract—The maximum clique of a given graph G is the subgraph C of G such that two vertices in C are adjacent in G with maximum cardinality. Finding the maximum clique in an arbitrary graph is an NPHard problem, motivated by the social networks analysis. In the real world applications, the nature ..."
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Abstract—The maximum clique of a given graph G is the subgraph C of G such that two vertices in C are adjacent in G with maximum cardinality. Finding the maximum clique in an arbitrary graph is an NPHard problem, motivated by the social networks analysis. In the real world applications, the nature of interaction between nodes is stochastic and the probability distribution function of the vertex weight is unknown. In this paper a learning automatabased algorithm is proposed for solving maximum clique problem in the stochastic graph. The simulation results on stochastic graph demonstrate that the proposed algorithm outperforms standard sampling method in terms of the number of samplings taken by algorithm. Keywords maximum clique problem; NPHard; stochastic graph; learning automata; social networks. I.
Tracking Extrema in Dynamic Environments Using a Learning AutomataBased Immune Algorithm
 in Grid and Distributed Computing, Control and Automation
, 2010
"... Abstract. In recent years, bioinspired algorithms have increasingly been used by researchers for solving various optimization problems increasingly. Many real world problems are mostly time varying optimization problems, which require special mechanisms for detecting changes in environment and then ..."
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Abstract. In recent years, bioinspired algorithms have increasingly been used by researchers for solving various optimization problems increasingly. Many real world problems are mostly time varying optimization problems, which require special mechanisms for detecting changes in environment and then responding to them. The present paper has been proposed to combination the learning automata and artificial immune algorithm in order to improve the performance of immune system algorithm in dynamic environments. In the proposed algorithm, the immune cells are equipped with a learning automaton. So they can increase diversity in response the dynamic environments. Learning automata based immune algorithm for dynamic environment has been tested in the moving parabola as a popular standard dynamic environment and compared by several famous algorithms in dynamic environments.
A Robust Heuristic Algorithm for Cooperative Particle Swarm Optimizer: A Learning Automata Approach
"... Abstract This paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behavior of swarms and learning ability of an automaton. This approach called the Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm uses th ..."
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Abstract This paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behavior of swarms and learning ability of an automaton. This approach called the Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm uses threelayer cooperation: intra swarm, inter swarm and inter population. There are two active populations in CPSOLA. In the primary population, the particles are placed in all swarms and each swarm consist of multiple dimensions of search space. Also there is a secondary population in CPSOLA which is used the conventional PSO's updating format. In the upper layer of cooperation, the embedded Learning Automaton (LA) is responsible for deciding whether to cooperate between populations or not. Experiments are organized on five benchmark functions and results show notable performance and robustness of CPSOLA, cooperative behavior of swarms and successful adaptive control of populations.
LADE: LEARNING AUTOMATA BASED DIFFERENTIAL EVOLUTION
, 2014
"... Accepted (Day Month Year) Many engineering optimization problems have not standard mathematical techniques, and cannot be solved using exact algorithms. Evolutionary algorithms have been successfully used for solving such optimization problems. Differential evolution is a simple and efficient popula ..."
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Accepted (Day Month Year) Many engineering optimization problems have not standard mathematical techniques, and cannot be solved using exact algorithms. Evolutionary algorithms have been successfully used for solving such optimization problems. Differential evolution is a simple and efficient populationbased evolutionary algorithm for global optimization, which has been applied in many real world engineering applications. However, the performance of this algorithm is sensitive to appropriate choice of its parameters as well as its mutation strategy. In this paper, we propose two different underlying classes of learning automata based differential evolution for adaptive selection of crossover probability and mutation strategy in differential evolution. In the first class, genomes of the population use the same mutation strategy and crossover probability. In the second class, each genome of the population adjusts its own mutation strategy and crossover probability parameter separately. The performance of the proposed methods is analyzed on ten benchmark functions from CEC 2005 and one reallife optimization problem. The obtained results show the
1 A Learning Automata Approach to Cooperative Particle Swarm Optimizer
"... This paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behavior of swarms and learning ability of an automaton. The approach is called Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm utilizes three laye ..."
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This paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behavior of swarms and learning ability of an automaton. The approach is called Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm utilizes three layers of cooperation which are intra swarm, inter swarm and inter population. There are two active populations in CPSOLA. In the primary population, the particles are placed in all swarms and each swarm consists of multiple dimensions of search space. Also there is a secondary population in CPSOLA which is used the conventional PSO's evolution schema. In the upper layer of cooperation, the embedded Learning Automaton (LA) is responsible for deciding whether to cooperate between these two populations or not. Experiments are organized on five benchmark functions and results show notable performance and robustness of CPSOLA, cooperative behavior of swarms and successful adaptive control of populations.
Physica A 396 (2014) 224–234 Contents lists available at ScienceDirect
"... journal homepage: www.elsevier.com/locate/physa Sampling from complex networks using distributed ..."
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journal homepage: www.elsevier.com/locate/physa Sampling from complex networks using distributed
Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC2010) An Adaptive Mutation Operator for Artificial Immune Network Using Learning Automata in Dynamic Environments
"... Abstract—Many real world problems are mostly time varying optimization problems, which require special mechanisms for detection changes in environment and then response to them. This paper proposes a hybrid optimization method based on learning automata and artificial immune network for dynamic envi ..."
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Abstract—Many real world problems are mostly time varying optimization problems, which require special mechanisms for detection changes in environment and then response to them. This paper proposes a hybrid optimization method based on learning automata and artificial immune network for dynamic environments, in which the learning automata are embedded in the immune cells to enhance its search capability via adaptive mutation, so they can increase diversity in response to the dynamic environments. The proposed algorithm is employed to deal with benchmark optimization problems under dynamic environments. Simulation results demonstrate the enhancements of our algorithm in tracking varying optima.
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Finding a Maximum Clique using Ant Colony Optimization and Particle Swarm Optimization in Social Networks
"... Abstract—Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this ..."
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Abstract—Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization algorithm. In the proposed method, in order to attain better results, it is improved process of pheromone update by particle swarm optimization. Simulation results on popular standard social network benchmarks in comparison standard ant colony optimization algorithm are shown a relative enhancement of proposed algorithm. Keywords social network analysis; clique problem; ACO; PSO. I.
Designing a Robust Heuristic for Cooperative Particle Swarm Optimizer: A Learning Automata Approach
"... Abstract: This paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behaviour of swarms and learning ability of an automaton. This approach called the Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm uses t ..."
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Abstract: This paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behaviour of swarms and learning ability of an automaton. This approach called the Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm uses threelayer cooperation: intra swarm, inter swarm and inter population. There are two active populations in CPSOLA. In the primary population, the particles are placed in all swarms and each swarm consist of multiple dimensions of search space. There is a secondary population in CPSOLA which is used the conventional PSO's updating format. In the upper layer of cooperation, the embedded Learning Automaton (LA) is responsible for deciding to cooperate between populations or not. Experiments are organized on five benchmark functions. The results were exhibited notable performance and robustness of CPSOLA, cooperative behavior of swarms and successful adaptive control of populations.