Hybrid scatter tabu search for unconstrained global optimization

Duarte, Abraham; Martı́, Rafael; Glover, Fred; Gortázar, Francisco

doi:10.1007/s10479-009-0596-2

Cited by 48 publications

(46 citation statements)

References 32 publications

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“…We point out that, similar procedures based on design of experiments have been used in the literature to generate populations of diverse solutions. See for instance the implementations in , (Duarte, Glover, Martí, & Gortázar, 2011) and The solutions in are evaluated by calling the black box. That is, the objective function value and feasibility of the solutions are known for each solution .…”

Section: Diversification Generation Methodsmentioning

confidence: 99%

A black-box scatter search for optimization problems with integer variables

et al. 2013

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The goal of this work is the development of a black-box solver based on the scatter search methodology. In particular, we seek a solver capable of obtaining high quality outcomes to optimization problems for which solutions are represented as a vector of integer values. We refer to these problems as integer optimization problems. We assume that the decision variables are bounded and that there may be constraints that require that the black-box evaluator is called in order to know whether they are satisfied. Problems of this type are common in operational research areas of applications such as telecommunications, project management, engineering design and the like. Our experimental testing includes 171 instances within four classes of problems taken from the literature. The experiments compare the performance of the proposed method with both the best context-specific procedures designed for each class of problem as well as context-independent commercial software. The experiments show that the proposed solution method competes well against commercial software and that can be competitive with specialized procedures in some problem classes.

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Section: Diversification Generation Methodsmentioning

confidence: 99%

A black-box scatter search for optimization problems with integer variables

et al. 2013

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“…Tabu search (TS) is a well-known meta-heuristic (Glover, 1989;Glover, 1990) which has proven to be effective for solving a large number of practical optimization problems, including quadratic assignment (James et al, 2009), unconstrained global optimization (Duarte et al, 2011), course timetabling (Lü and Hao, 2010), graph coloring (Wu and Hao, 2012), etc. For CODP, this paper employs a TS procedure as follows.…”

Section: Tabu Search Proceduresmentioning

confidence: 99%

Iterated tabu search for the circular open dimension problem

Huang

2013

European Journal of Operational Research

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This paper mainly investigates the circular open dimension problem (CODP), which consists of packing a set of circles of known radii into a strip of fixed width and unlimited length without overlapping. The objective is to minimize the length of the strip. An iterated tabu search approach, named ITS, is proposed. ITS starts from a randomly generated solution and attempts to gain improvements by a tabu search procedure. After that, if the obtained solution is not feasible, a perturbation operator is subsequently employed to reconstruct the incumbent solution and an acceptance criterion is implemented to determine whether or not accept the perturbed solution. This process is repeated until a feasible solution has been found or the allowed computation time has been elapsed. Computational experiments based on well-known benchmark instances show that ITS produces quite competitive results with respect to the best known results. For 18 representative CODP instances taken from the literature, ITS succeeds in improving 13 best known results within reasonable time. In addition, for another challenging related variant: the problem of packing arbitrary sized circles into a circular container, ITS also succeeds in improving many best known results. Supplementary experiments are also provided to analyze the influence of the perturbation operator, as well as the acceptance criterion.

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“…Yin et al (2010) have shown the advantages of the original CyberSA by comparing it with several other metaheuristics such as the Standard PSO 2007 (Clerc, 2008), C-GRASP (Hirsch et al, 2007), Direct Tabu Search (Hedar & Fukushima, 2006), Scatter Search (Laguna & Marti, 2005), and Hybrid Scatter Tabu Search (Duarte et al, 2011a). In our present comparison we also include the PSwarm algorithm of Vaz and Vicente (2007), which embeds the swarm algorithm into the pattern search framework.…”

Section: Performance Measures and Competing Algorithmsmentioning

confidence: 99%

A Complementary Cyber Swarm Algorithm

Yin

Glover

Laguna

et al. 2013

Recent Algorithms and Applications in Swarm Intelligence Research

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A recent study (Yin et al., 2010) Standard PSO 2007 method (Clerc, 2008. showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to thewere previously found as a basis for focusing the search in regions anticipated to harbor additional solutions of high quality. Diversification promotes the exploration of regions appreciably different from those previously examined in order to produce new solutions with characteristics that depart from those already seen. Intensification and diversification work together to identify new promising regions when the slave heuristics stagnate in the executed search courses. Many intelligent algorithms fall in the territory of metaheuristics. DOI: 10.4018/jsir.2011040102 International Journal of Swarm Intelligence Research, 2(2), 22-41, April-June 2011 23 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.Some exemplary algorithms (Luke, 2009) are genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), tabu search (TS), particle swarm optimization (PSO), scatter search (SS), greedy randomized adaptive search procedure (GRASP), variable neighborhood search (VNS), to name a few. A recent survey and descriptive analysis of metaheuristic algorithms can be found in Sorensen and Glover (2010).Slave heuristics embedded in metaheuristic methods often adopt solution combination or neighborhood exploration processes to generate new solutions based on the current state of search. Solution combination approaches produce new solutions by exchanging information between candidate solutions (for example, crossover operation executed in GA) or by using candidate solutions as guiding points for producing new solutions (for example, by reference to the best experiences in PSO or the path relinking (PR) process used in SS). Alternatively, neighborhood exploration employs incremental changes, called ...

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Hybrid scatter tabu search for unconstrained global optimization

Cited by 48 publications

References 32 publications

A black-box scatter search for optimization problems with integer variables

A black-box scatter search for optimization problems with integer variables

Iterated tabu search for the circular open dimension problem

A Complementary Cyber Swarm Algorithm

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