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A multiobjective evolutionary algorithm based on decomposition
 IEEE Transactions on Evolutionary Computation, Accepted
, 2007
"... 1 Decomposition is a basic strategy in traditional multiobjective optimization. However, this strategy has not yet widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a MOP into a number o ..."
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Cited by 44 (14 self)
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1 Decomposition is a basic strategy in traditional multiobjective optimization. However, this strategy has not yet widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a MOP into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and NSGAII. Experimental results show that it outperforms or performs similarly to MOGLS and NSGAII on multiobjective 01 knapsack problems and continuous multiobjective optimization problems. Index Terms multiobjective optimization, decomposition, evolutionary algorithms, memetic algorithms, Pareto optimality, computational complexity. I.
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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Cited by 8 (5 self)
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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.
Finding Maximum Clique in Stochastic Graphs Using Distributed Learning Automata
 International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems
, 2015
"... Because of unpredictable, uncertain and timevarying nature of real networks it seems that stochastic graphs, in which weights associated to the edges are random variables, may be a better candidate as a graph model for real world networks. Once the graph model is chosen to be a stochastic graph, ev ..."
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Cited by 4 (2 self)
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Because of unpredictable, uncertain and timevarying nature of real networks it seems that stochastic graphs, in which weights associated to the edges are random variables, may be a better candidate as a graph model for real world networks. Once the graph model is chosen to be a stochastic graph, every feature of the graph such as path, clique, spanning tree and dominating set, to mention a few, should be treated as a stochastic feature. For example, choosing stochastic graph as the graph model of an online social network and defining community structure in terms of clique, and the associations among the individuals within the community as random variables, the concept of stochastic clique may be used to study community structure properties. In this paper maximum clique in stochastic graph is first defined and then several learning automatabased algorithms are proposed for solving maximum clique problem in stochastic graph where the probability distribution functions of the weights associated with the edges of the graph are unknown. It is shown that by a proper choice of the parameters of the proposed algorithms, one can make the probability of finding maximum clique in stochastic graph as close to unity as possible. Experimental results show that the proposed algorithms significantly reduce the number of samples needed to be taken from the edges of the stochastic graph as compared to the number of samples needed by standard sampling method at a given confidence level.
The Automatic Design of Hyperheuristic Framework with Gene Expression Programming for Combinatorial Optimization problems
"... IEEE Abstract—Hyperheuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailormade methodologies for individual problems. Hyperheuristics accomplish this through a high level heuristic (heuristic selection mechanism and an acceptance crite ..."
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IEEE Abstract—Hyperheuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailormade methodologies for individual problems. Hyperheuristics accomplish this through a high level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems or even instances, have different landscape structures and complexity, the design of efficient high level heuristics can have a dramatic impact on hyperheuristic performance. In this work, instead of using human knowledge to design the high level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance solving process, the high level heuristic of the hyperheuristic
MEDACO: Solving Multiobjective Combinatorial Optimization with Evolution, Decomposition and Ant Colonies.
"... We propose a novel multiobjective evolutionary algorithm, MEDACO, a shorter acronym for MOEA/DACO, combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The motivation is to use the onlinelearning capabilities of ACO, according to the Re ..."
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We propose a novel multiobjective evolutionary algorithm, MEDACO, a shorter acronym for MOEA/DACO, combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The motivation is to use the onlinelearning capabilities of ACO, according to the Reactive Search Optimization (RSO) paradigm of ”learning while optimizing”, to further improve the effectiveness of the original MOEA/D algorithms. Following other MOEA/Dlike algorithms, MEDACO decomposes a multiobjective optimization problem into a number of singleobjective optimization tasks solved by different iterated greedy construction processes (a.k.a. ants). Each ant has an individual heuristic information matrix and several neighboring ants, characterized by a similar combination of the individual objectives. All ants are divided into groups, with each group maintaining a different pheromone matrix. During the search, each ant records the best solution found so far for its subproblem. To construct a new solution, an ant combines information from its group’s pheromone matrix, its own heuristic information matrix and its current solution. Extensive experimental comparisons are executed. On the multiobjective 01 knapsack problem, MEDACO outperforms MOEA/DGA on all the nine test instances. Furthermore, we demonstrate that the heuristic information matrices in MEDACO are crucial to significantly improve the performance. On the biobjective traveling salesman problem, MEDACO performs much better than the previously proposed BicriterionAnt algorithm on the 12 test instances. We also critically evaluate the effects of the group, the neighborhood and the location information of current solutions on the performance of MEDACO.
Multiobjective Combinatorial Optimization by Using Decomposition and Ant Colony
"... Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/DACO. Following other MOEA/Dlike algorithms, MOEA/DACO decomposes a multiobjective optimization problem into a numbe ..."
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Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/DACO. Following other MOEA/Dlike algorithms, MOEA/DACO decomposes a multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving one subproblem. All the ants are divided into a few groups and each ant has several neighboring ants. An ant group maintains a pheromone matrix and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its subproblem. To construct a new solution, an ant combines information from its group’s pheromone matrix, its own heuristic information matrix and its current solution. An ant checks the new solutions constructed by itself and its neighbors, and updates its current solution if it has found a better one in terms of its own objective. Extensive experiments have been conducted in this paper to study and compare MOEA/DACO with other algorithms on two set of test problems. On the multiobjective 01 knapsack problem, MOEA/DACO outperforms MOEA/DGA on all the nine test instances. We also demonstrate that the heuristic information matrices in MOEA/DACO are crucial to the good performance of MOEA/DACO for the knapsack problem. On the biobjective traveling salesman problem, MOEA/DACO performs much better than BicriterionAnt on all the 12 test instances. We also evaluate the effects of grouping, neighborhood and the location information of current solutions on the performance of MOEA/DACO. The work in this paper shows that reactive search optimization scheme, i.e., the “learning while optimizing” principle, is effective in improving multiobjective optimization algorithms.