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Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 294 (16 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues
 IEEE Transactions on Evolutionary Computation
, 2005
"... We recommend you cite the published version. ..."
Computational models and heuristic methods for Grid scheduling problems
 FUTURE GENERATION COMPUTER SYSTEMS
, 2010
"... ..."
A continuous variable neighbourhood search based on specialised EAs: Application to the noisy BBObenchmark 2009 testbed
 In Genetic Evolutionary Computation Conf
, 2009
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 23 (2 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
The Design of Memetic Algorithms for Scheduling and Timetabling Problems
 Recent Advances in Memetic Algorithms, Studies in Fuzziness and Soft Computing
, 2004
"... Summary. There are several characteristics that make scheduling and timetabling problems particularly difficult to solve: they have huge search spaces, they are often highly constrained, they require sophisticated solution representation schemes, and they usually require very timeconsuming fitness ..."
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Summary. There are several characteristics that make scheduling and timetabling problems particularly difficult to solve: they have huge search spaces, they are often highly constrained, they require sophisticated solution representation schemes, and they usually require very timeconsuming fitness evaluation routines. There is a considerable number of memetic algorithms that have been proposed in the literature to solve scheduling and timetabling problems. In this chapter, we concentrate on identifying and discussing those strategies that appear to be particularly useful when designing memetic algorithms for this type of problems. For example, the many different ways in which knowledge of the problem domain can be incorporated into memetic algorithms is very helpful to design effective strategies to deal with infeasibility of solutions. Memetic algorithms employ local search, which serves as an effective intensification mechanism that is very useful when using sophisticated representation schemes and timeconsuming fitness evaluation functions. These algorithms also incorporate a population, which gives them an effective explorative ability to sample huge search spaces. Another important aspect that has been investigated when designing memetic algorithms for scheduling and timetabling problems, is how to establish the right balance between the work performed by the genetic search and the work performed by the local search. Recently, researchers have put considerable attention in the design of selfadaptive memetic algorithms. That is, to incorporate memes that adapt themselves according to the problem domain being solved and also to the particular conditions of the search process. This chapter also discusses some recent ideas proposed by researchers that might be useful when designing selfadaptive memetic algorithms. Finally, we give a summary of the issues discussed throughout the chapter and propose some future research directions in the design of memetic algorithms for scheduling and timetabling problems. 1
A Unified View on Hybrid Metaheuristics
, 2006
"... Abstract. Manifold possibilities of hybridizing individual metaheuristics with each other and/or with algorithms from other fields exist. A large number of publications documents the benefits and great success of such hybrids. This article overviews several popular hybridization approaches and class ..."
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Abstract. Manifold possibilities of hybridizing individual metaheuristics with each other and/or with algorithms from other fields exist. A large number of publications documents the benefits and great success of such hybrids. This article overviews several popular hybridization approaches and classifies them based on various characteristics. In particular with respect to lowlevel hybrids of different metaheuristics, a unified view based on a common pool template is described. It helps in making similarities and different key components of existing metaheuristics explicit. We then consider these key components as a toolbox for building new, effective hybrid metaheuristics. This approach of thinking seems to be superior to sticking too strongly to the philosophies and historical backgrounds behind the different metaheuristic paradigms. Finally, particularly promising possibilities of combining metaheuristics with constraint programming and integer programming techniques are highlighted. 1
Gradientbased/Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the SMetric
, 2007
"... The computation of a good approximation set of the Pareto front of a multiobjective optimization problem can be recasted as the maximization of its Smetric value. A highprecision method for computing approximation sets of a Pareto front with maximal SMetric is presented in this paper as a highl ..."
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Cited by 10 (1 self)
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The computation of a good approximation set of the Pareto front of a multiobjective optimization problem can be recasted as the maximization of its Smetric value. A highprecision method for computing approximation sets of a Pareto front with maximal SMetric is presented in this paper as a highlevel relay hybrid of an evolutionary algorithm and a gradient method, both guided by the Smetric. First, an evolutionary multiobjective optimizer moves the initial population close to the Pareto front. The gradientbased method takes this population as its starting point for computing a local maximal approximation set with respect to the Smetric. As opposed to existing work on gradientbased multicriteria optimization in the new gradient approach we compute gradients based on a set of points rather than for single points. We will term this approach indicatorbased gradient method, and exemplify it for the Smetric. We derive expressions for computing the gradient of a set of points with respect
Metaheuristics for Grid Scheduling Problems
, 2008
"... In this chapter, we review a few important concepts from Grid computing related to scheduling problems and their resolution using heuristic and metaheuristic approaches. Scheduling problems are at the heart of any Gridlike computational system. Different types of scheduling based on different cri ..."
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Cited by 7 (1 self)
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In this chapter, we review a few important concepts from Grid computing related to scheduling problems and their resolution using heuristic and metaheuristic approaches. Scheduling problems are at the heart of any Gridlike computational system. Different types of scheduling based on different criteria, such as static vs. dynamic environment, multiobjectivity, adaptivity, etc., are identified. Then, heuristics and metaheuristics methods for scheduling in Grids are presented. The chapter reveals the complexity of the scheduling problem in Computational Grids when compared to scheduling in classical parallel and distributed systems and shows the usefulness of heuristics and metaheuristics approaches for the design of efficient Grid schedulers.
Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic AlgorithmTabu Search
"... Abstract: This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restock ..."
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Abstract: This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restocking. The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities resulted.
Hybrid Genetic Algorithms: A Review
"... Abstract—Hybrid genetic algorithms have received significant interest in recent years and are being increasingly used to solve realworld problems. A genetic algorithm is able to incorporate other techniques within its framework to produce a hybrid that reaps the best from the combination. In this p ..."
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Abstract—Hybrid genetic algorithms have received significant interest in recent years and are being increasingly used to solve realworld problems. A genetic algorithm is able to incorporate other techniques within its framework to produce a hybrid that reaps the best from the combination. In this paper, different forms of integration between genetic algorithms and other search and optimization techniques are reviewed. This paper also aims to examine several issues that need to be taken into consideration when designing a hybrid genetic algorithm that uses another search method as a local search tool. These issues include the different approaches for employing local search information and various mechanisms for achieving a balance between a global genetic algorithm and a local search method. Index Terms—Genetic algorithms, evolutionary computation, hybrid genetic algorithms, geneticlocal hybrid algorithms, memetic algorithms, Lamarckian search, Baldwinian search. I.