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31
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 45 (15 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.
Randomized tree construction algorithm to explore energy landscapes
 J. Comput. Chem
"... We report in the present work a new method for exploring conformational energy landscapes. The method, called TRRT, combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is ..."
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Cited by 11 (6 self)
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We report in the present work a new method for exploring conformational energy landscapes. The method, called TRRT, combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: on the one hand, it is naturally biased towards yet unexplored regions of the space; on the other, a Monte Carlolike transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved thanks to a selftuning mechanism. The method is able to efficiently find both, energy minima and transition paths between them. As a proof of concept, the method is applied to two academic benchmarks and to the alanine dipeptide.
Breakout local search for maximum clique problems
 Computers & Operations Research
"... The maximum clique problem (MCP) is one of the most popular combinatorial optimization problems with various practical applications. An important generalization of MCP is the maximum weight clique problem (MWCP) where a positive weight is associate to each vertex. In this paper, we present Breakout ..."
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Cited by 9 (6 self)
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The maximum clique problem (MCP) is one of the most popular combinatorial optimization problems with various practical applications. An important generalization of MCP is the maximum weight clique problem (MWCP) where a positive weight is associate to each vertex. In this paper, we present Breakout Local Search (BLS) which can be applied to both MC and MWC problems without any particular adaptation. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. Extensive experimental evaluations using the DIMACS and BOSHLIB benchmarks show that the proposed approach competes favourably with the current stateofart heuristic methods for MCP. Moreover, it is able to provide some new improved results for a number of MWCP instances. This paper also reports for the first time a detailed landscape analysis, which has been missing in the literature. This analysis not only explains the difficulty of several benchmark instances, but also justifies to some extent the behaviour of the proposed approach and the used parameter settings.
Iterativedeepening search with online tree size prediction
 In International Conference on Learning and Intelligent Optimization (LION
, 2012
"... eaburns at cs.unh.edu and ruml at cs.unh.edu Abstract. The memory requirements of bestfirst graph search algorithms such as A * often prevent them from solving large problems. The bestknown approach for coping with this issue is iterative deepening, which performs a series of bounded depthfirst s ..."
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Cited by 8 (2 self)
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eaburns at cs.unh.edu and ruml at cs.unh.edu Abstract. The memory requirements of bestfirst graph search algorithms such as A * often prevent them from solving large problems. The bestknown approach for coping with this issue is iterative deepening, which performs a series of bounded depthfirst searches. Unfortunately, iterative deepening only performs well when successive cost bounds visit a geometrically increasing number of nodes. While it happens to work acceptably for the classic sliding tile puzzle, IDA * fails for many other domains. In this paper, we present an algorithm that adaptively chooses appropriate cost bounds online during search. During each iteration, it learns a model of the search tree that helps it to predict the bound to use next. Our search tree model has three main benefits over previous approaches: 1) it will work in domains with realvalued heuristic estimates, 2) it can be trained online, and 3) it is able to make predictions with only a small number of training examples. We demonstrate the power of our improved model by using it to control an iterativedeepening A* search online. While our technique has more overhead than previous methods for controlling iterativedeepening A*, it can give more robust performance by using its experience to accurately double the amount of search effort between iterations. 1
An efficient algorithm for the provision of a dayahead modulation service by a load aggregator
 Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES
, 2013
"... Abstract—This article studies a decision making problem faced by an aggregator willing to offer a load modulation service to a Transmission System Operator. This service is contracted one day ahead and consists in a load modulation option, which can be called once per day. The option specifies the r ..."
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Cited by 7 (6 self)
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Abstract—This article studies a decision making problem faced by an aggregator willing to offer a load modulation service to a Transmission System Operator. This service is contracted one day ahead and consists in a load modulation option, which can be called once per day. The option specifies the range of a potential modification on the demand of the loads within a certain time interval. The specific case where the loads can be modeled by a generic tank model is considered. Under this assumption, the problem of maximizing the range of the load modulation service can be formulated as a mixed integer linear programming problem. A novel heuristicmethod is proposed to solve this problem in a computationally efficient manner. This method is tested on a set of problems. The results show that this approach can be orders of magnitude faster than CPLEX without significantly degrading the solution accuracy.
Breakout Local Search for the MaxCut Problem
, 2012
"... Given an undirected graph G = (V, E) where each edge of E is weighted with an integer number, the maximum cut problem (MaxCut) is to partition the vertices of V into two disjoint subsets so as to maximize the total weight of the edges between the two subsets. As one of Karp’s 21 NPcomplete problem ..."
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Cited by 7 (5 self)
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Given an undirected graph G = (V, E) where each edge of E is weighted with an integer number, the maximum cut problem (MaxCut) is to partition the vertices of V into two disjoint subsets so as to maximize the total weight of the edges between the two subsets. As one of Karp’s 21 NPcomplete problems, MaxCut has attracted considerable attention over the last decades. In this paper, we present Breakout Local Search (BLS) for MaxCut. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. The proposed algorithm shows excellent performance on the set of wellknown maximum cut benchmark instances in terms of both solution quality and computational time. Out of the 71 benchmark instances, BLS is capable of finding new improved results in 33 cases and attaining the previous bestknown result for 35 instances, within a computational time ranging from less than one second to 5.6 hours for the largest instance with 20000 vertices.
REVO: a Reactive Evolutionary Algorithm for the Maximum Clique Problem
, 2007
"... Abstract—An evolutionary algorithm with guided mutation (EA/G) has been proposed recently for solving the maximum clique problem. In the framework of estimationofdistribution algorithms (EDA), guided mutation uses a model distribution to generate the offspring by combining the local information of ..."
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Cited by 7 (3 self)
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Abstract—An evolutionary algorithm with guided mutation (EA/G) has been proposed recently for solving the maximum clique problem. In the framework of estimationofdistribution algorithms (EDA), guided mutation uses a model distribution to generate the offspring by combining the local information of solutions found so far with global statistical information. Each individual is then subjected to a Marchiori’s repair heuristic, based on randomized extraction and greedy expansion, to ensure that it represents a legal clique. The novel reactive and evolutionary algorithm (REVO) proposed in this paper starts from the same evolutionary framework but considers more complex individuals, which modify tentative solutions by local search with memory, in the reactive search framework. In particular, the estimated distribution is used to periodically initialize the state of each individual based on the previous statistical knowledge extracted from the population. We demonstrate that the combination of the estimationofdistribution concept with reactive search produces significantly better results than EA/G and is remarkably robust w.r.t. the setting of the algorithm parameters. REVO adopts a drastically simplified lowknowledge version of reactive local search (RLS), with a simple internal diversification mechanism based on tabusearch, with a prohibition parameter proportional to the estimated best clique size. REVO is competitive with the more complex fullknowledge RLSEVO which adopts the original RLS algorithm. For most of the benchmark instances, the hybrid scheme version produces significantly better results than EA/G for comparable or smaller CPU times.
Active Learning of Combinatorial Features for Interactive Optimization
"... Abstract. We address the problem of automated discovery of preferred solutions by an interactive optimization procedure. The algorithm iteratively learns a utility function modeling the quality of candidate solutions and uses it to generate novel candidates for the following refinement. We focus on ..."
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Cited by 5 (2 self)
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Abstract. We address the problem of automated discovery of preferred solutions by an interactive optimization procedure. The algorithm iteratively learns a utility function modeling the quality of candidate solutions and uses it to generate novel candidates for the following refinement. We focus on combinatorial utility functions made of weighted conjunctions of Boolean variables. The learning stage exploits the sparsityinducing property of 1norm regularization to learn a combinatorial function from the power set of all possible conjunctions up to a certain degree. The optimization stage uses a stochastic local search method to solve a weighted MAXSAT problem. We show how the proposed approach generalizes to a large class of optimization problems dealing with satisfiability modulo theories. Experimental results demonstrate the effectiveness of the approach in focusing towards the optimal solution and its ability to recover from suboptimal initial choices. 1