scispace - formally typeset
Search or ask a question

Showing papers on "Metaheuristic published in 1993"


Journal ArticleDOI
TL;DR: A hierarchical algorithm for the flexible job shop scheduling problem is described, based on the tabu search metaheuristic, which allows to adapt the same basic algorithm to different objective functions.
Abstract: A hierarchical algorithm for the flexible job shop scheduling problem is described, based on the tabu search metaheuristic. Hierarchical strategies have been proposed in the literature for complex scheduling problems, and the tabu search metaheuristic, being able to cope with different memory levels, provides a natural background for the development of a hierarchical algorithm. For the case considered, a two level approach has been devised, based on the decomposition in a routing and a job shop scheduling subproblem, which is obtained by assigning each operation of each job to one among the equivalent machines. Both problems are tackled by tabu search. Coordination issues between the two hierarchical levels are considered. Unlike other hierarchical schemes, which are based on a one-way information flow, the one proposed here is based on a two-way information flow. This characteristic, together with the flexibility of local search strategies like tabu search, allows to adapt the same basic algorithm to different objective functions. Preliminary computational experience is reported.

874 citations


Journal ArticleDOI
TL;DR: This paper applies the tabu-search technique to the job-shop scheduling problem, a notoriously difficult problem in combinatorial optimization and shows that the implementation of this method dominates both a previous approach with tabu search and the other heuristics based on iterative improvements.
Abstract: In this paper, we apply the tabu-search technique to the job-shop scheduling problem, a notoriously difficult problem in combinatorial optimization. We show that our implementation of this method dominates both a previous approach with tabu search and the other heuristics based on iterative improvements.

605 citations


Book
01 Feb 1993
TL;DR: The linear optimization and extensions theory and algorithms is one book that the authors really recommend you to read, to get more solutions in solving this problem.
Abstract: A solution to get the problem off, have you found it? Really? What kind of solution do you resolve the problem? From what sources? Well, there are so many questions that we utter every day. No matter how you will get the solution, it will mean better. You can take the reference from some books. And the linear optimization and extensions theory and algorithms is one book that we really recommend you to read, to get more solutions in solving this problem.

259 citations


Book ChapterDOI
01 Jan 1993
TL;DR: A stochastic search procedure that is the basis of genetic algorithms (GA), is described, in developing near-optimal topologies of load bearing truss structures, an adaptation of the ground-structure method of topology optimization.
Abstract: The present paper describes the use of a stochastic search procedure that is the basis of genetic algorithms (GA), in developing near-optimal topologies of load bearing truss structures The problem addressed is one wherein the structural geometry is created from a specification of load conditions and available support points in the design space The development of this geometry must satisfy kinematic stability requirements in addition to the usual requirements of structural strength and stiffness The approach is an adaptation of the ground-structure method of topology optimization, and is implemented in a two-level GA based search In this process, the kinematic stability constraints are imposed at one level, followed by the treatment of response constraints at a second level of optimization Singular value decomposition is used to assess the kinematic stability constraint at the first level of design, and results in the creation of a finite number of increasing weight, stable topologies Member sizing is then introduced at a second level of design, where minimal weight and response constraints are simultaneously considered At this level, the only admissible topologies are those identified during the first stage and any stable combinations thereof The design variable representation scheme allows for both the removal and addition of structural members during optimization

132 citations


Journal ArticleDOI
TL;DR: This paper proposes that hash functions be used to record the solutions encountered during recent iterations of the search in a long list to free the algorithm designer of the need to consider cycling when creating tabu restrictions based on move attributes.
Abstract: Tabu search as proposed by Glover [3,4] has proven to be a very effective metaheuristic for hard problems. In this paper we propose that hash functions be used to record the solutions encountered during recent iterations of the search in a long list. Hash values of potential solutions can be compared to the values on the list for the purpose of avoiding cycling. This frees the algorithm designer of the need to consider cycling when creating tabu restrictions based on move attributes. We suggest specific functions that result in very good performance.

128 citations


Proceedings Article
24 Aug 1993
TL;DR: The co.91 of query optimization is ufiected by both the ~eurch apocc und the search atmlegy of the opti,aizer, and the tmde-o# between optimiaution cost and pumllel ezecution coat using the DBS$ pumllC1 query optimizer is investigated.
Abstract: The co.91 of query optimization is ufiected by both the ~eurch apocc und the search atmlegy of the opti,aizer. I~I (1 pumllel ecectrlion environment, Ihe search ap~cc tends lo he much lurger than in the centmlized ca,sc-. This is due to the high number of ezecution okeruutivrs which implies a aignijiconl increase in the op~imisutiofc coal. 118 &a puper, we investigate the tmde-o# between optimiaution cost and pumllel ezecution coat using the DBS$ pumllC1 query optimizer. We describe its cost model which cuptrrrea ull esaentiol aapects of pumllel executions. We show how the coal melricu imply a aignijiconl increase in the search spuce and oplimirotion coat. Howeuer, inateud of restricting lhe aeurch spabe, which muy lead to loosing letter plans, we reduce the optimization cost Iv controlling the search slmtegy. We ezrend mndomiaed strulegiea to adopt well lo pamllel query optimization. In particular, we propose Toured Simulated Annealing which provides u letter tmdec$ letween optimisa#on cost and yuulity of Ura ~~rullel r+rculiorr plan.

122 citations


Journal ArticleDOI
TL;DR: The SA algorithm is found to be able to provide good, if not better, solutions when compared to existing mixed-discrete optimization algorithms based on the authors' investigations and studies.
Abstract: A global optimization algorithm for solving mixed-discrete nonlinear optimization problems is developed and presented. The algorithm makes use of a stochastic optimization technique - simulated annealing (SA). Approaches to handle constraints and various types of variables are discussed. The SA algorithm is found to be able to provide good, if not better, solutions when compared to existing mixed-discrete optimization algorithms based on the authors' investigations and studies. The performance of the algorithm and comparisons between SA and those algorithms are demonstrated through 17 test problems

116 citations


Journal ArticleDOI
TL;DR: A dynamic strategy, the reverse elimination method, for tabu list management, is described and directions on improving its computational effort are given.
Abstract: Tabu search is a metastrategy for guiding known heuristics to overcome local optimality. Successful applications of this kind of metaheuristic to a great variety of problems have been reported in the literature. However, up to now mainly static tabu list management ideas have been applied. In this paper we describe a dynamic strategy, the reverse elimination method, and give directions on improving its computational effort. The impact of the method will be shown with respect to a multiconstraint version of the zero-one knapsack problem. Numerical results are presented comparing it with a simulated annealing approach.

116 citations


Journal ArticleDOI
TL;DR: Population approaches suitable for global combinatorial optimization are discussed and their use to improve and benchmark the results by using tabu search as the individual optimization strategy within a population heuristic is suggested and explored.
Abstract: Population approaches suitable for global combinatorial optimization are discussed in this paper. They are composed of a number of distinguishable individuals called "agents", each one using a particular optimization strategy. Periods of independent search follow phases on which the population is restarted from new configurations. Due to its intrinsic parallelism and the asynchronicity of the method, it is particularly suitable for parallel computers. Results on two test problems are presented in this paper. The individual search optimization strategies for each agent have been chosen having the basic characteristics of tabu search. This has been done in order to avoid mixing the hypothesized properties of these population approaches with those of more elaborate tabu search strategies, but remarking on its main characteristics. A set of four test problem "landscapes" is discussed and their use to improve and benchmark the results by using tabu search as the individual optimization strategy within a population heuristic is suggested and explored. The application of tabu search to new problem areas, like molecular biology, is also investigated.

71 citations


Journal ArticleDOI
TL;DR: An implementation of simulated annealing and tabu search is described for discrete versions of two noxious facility location problems —p-dispersion andp-defense-sum and the performance is compared to a semi-greedy heuristic for thirty randomly generated 25-node data sets.
Abstract: An implementation of simulated annealing and tabu search is described for discrete versions of two noxious facility location problems —p-dispersion andp-defense-sum. A series of computational experiments leading to good choices of the parameters that drive simulated annealing and tabu search are presented for a 33-node data set. Using these parameter settings, the performance of simulated annealing and tabu search are compared to a semi-greedy heuristic for thirty randomly generated 25-node data sets.

59 citations



Book
01 Jan 1993
TL;DR: Equilibrium models of mathematical economy numerical optimization methods and software convex programming methods of optimal complexity polynomial algorithms in linear programming decomposition of optimization systems.
Abstract: Equilibrium models of mathematical economy numerical optimization methods and software convex programming methods of optimal complexity polynomial algorithms in linear programming decomposition of optimization systems modern apparatus of non-smooth optimization discrete programming models and methods analysis of inconsistent mathematical programming problems multiobjective problems optimization in order scales extremal problems in infinite-dimensional spaces.

Book ChapterDOI
01 Jan 1993
TL;DR: Constrained optimization is used for interactive surface design in the new surface editor and allows designers to modify B-spline surfaces to satisfy their design intents, expressed as geometric constraints.
Abstract: Constrained optimization is used for interactive surface design in our new surface editor. It allows designers to modify B-spline surfaces to satisfy their design intents, expressed as geometric constraints. The restrictions on the set of constraints are few. In the special case of no constraints a surface can be faired to remove design flaws.

Proceedings Article
01 Jun 1993
TL;DR: This paper presents simple analytical models of genetic algorithms which are commonly used in multimodal function optimization and their predictive value is veriied by running the corresponding genetic algorithm on various multi-modal functions of varying complexity.
Abstract: This paper presents simple analytical models of genetic algorithms which are commonly used in multimodal function optimization. The methodology for constructing the models is similar throughout the study. The predictive value of each model is veriied by running the corresponding genetic algorithm on various multimodal functions of varying complexity.

Journal ArticleDOI
TL;DR: Analytical investigations of simulated annealing and single-trial versions of evolution strategies lead to a cross-fertilization of both approaches, resulting in new theoretical results, new parallel population-based algorithms, and a better understanding of the interrelationships.
Abstract: Simulated annealing and single-trial versions of evolution strategies possess a close relationship when they are designed for optimization over continuous variables. Analytical investigations of their differences and similarities lead to a cross-fertilization of both approaches, resulting in new theoretical results, new parallel population-based algorithms, and a better understanding of the interrelationships.


Journal ArticleDOI
TL;DR: An adaptation of tabu search to GFCP is described and computational results are given and those obtained are compared with those obtained from SWIFT-2.
Abstract: Recent successes in applying tabu search to a wide variety of classical optimization problems have motivated the investigation of applying tabu search to the well-known general fixed charge problem (GFCP). In this paper, an adaptation of tabu search to GFCP is described and computational results are given. In addition, the computational results are compared with those obtained from SWIFT-2, the most well-known and frequently used heuristic method for GFCP. As will be shown, the results are very encouraging.

Proceedings Article
01 Jan 1993
TL;DR: An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization and the advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized.
Abstract: An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

Book ChapterDOI
01 Jan 1993
TL;DR: The main objective of this paper is an empirical analysis of different optimization algorithms and some of their combinations in comparison with a decision tree learning algorithm.
Abstract: Local optimization algorithms, standardly used in combinatorial optimization, can be applied in inductive concept learning. Learning can be defined as search of the space of concept descriptions, guided by some heuristic function. The paper presents learning with stochastic local optimization algorithms (based on Simulated annealing) and deterministic local optimization algorithms ( k-opt known from solving the ‘travelling salesman’ problem). These algorithms and some of their combinations have been tested within the ATRIS shell, and their performance compared on different real-world machine learning problems. The rule induction shell ATRIS was developed to enable simple use of existing and adding of new optimization algorithms and noise-handling mechanisms, to enable simple ‘cross validation’ experiments, experiments with different attributes considered as class and different attribute subsets used for learning. The main objective of this paper is an empirical analysis of different optimization algorithms and some of their combinations in comparison with a decision tree learning algorithm.

Proceedings ArticleDOI
01 Jan 1993
TL;DR: The genetic algorithm approach to the solution of the placement problem is described in detail and an example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms.
Abstract: Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.

Journal Article
TL;DR: Newton-like search directions that are appropriate when solving large-scale linearly-constrained nonlinear optimization problems are considered and ecient ways of modifying the Newton equations are considered in order to ensure global convergence of the underlying optimization methods.
Abstract: We consider the computation of Newton-like search directions that are appropriate when solving large-scale linearly-constrained nonlinear optimization problems. We investigate the use of both direct and iterative methods and consider ecient ways of modifying the Newton equations in order to ensure global convergence of the underlying optimization methods. in the directory \pub/reports".

Journal ArticleDOI
TL;DR: This work proposes, by analogy to the travelling salesman problem, a new method taking advantage of the capability of Hopfield-like neural networks to carry out combinatorial optimization of an objective function, which can also suggest partial solutions having one or two atoms less than the given pattern.
Abstract: The three-dimensional (3D)-pattern search problem can be summarized as finding, in a molecule, the subset of atoms that have the most similar spatial arrangement as those of a given 3D pattern. For this NP-complete combinatorial optimization problem we propose, by analogy to the travelling salesman problem, a new method taking advantage of the capability of Hopfield-like neural networks to carry out combinatorial optimization of an objective function. This objective function is built from the sum of the differences of interatomic distances in the pattern and the molecule. Here we present the implementation we have found of the 3D-pattern search problem on Hopfield-like neural networks. Initial tests indicate that this approach not only successfully retrieves a given pattern, but can also suggest partial solutions having one or two atoms less than the given pattern, an interesting feature in the case of local conformational flexibility of the molecule. The distributed representation of the problem...


Proceedings ArticleDOI
01 Dec 1993
TL;DR: The main objective of this paper is to combine the ideas of simplex method with the genetic algorithms (GAs) in order to give a hill-climbing ability to the conventional GAs, like neural networks, for function optimization.
Abstract: In general, one of the shortcomings in GAs as search methods is their lack of local search ability. The main objective of this paper is to combine the ideas of simplex method with the genetic algorithms (GAs). In order to give a hill-climbing ability to the conventional GAs, like neural networks, we propose PHGA for function optimization. Computer simulation results using De Jong's five-function test bed (1975) are shown. According to our simulation, all of the results by the proposed PHGA are better than those by the conventional GAs.

Proceedings ArticleDOI
11 Jan 1993



Journal ArticleDOI
TL;DR: In this article, a deterministic heuristic, tabu search, is presented for minimizing surface distortion in a two-ring, flat, tetrahedral truss structure, based on the influence matrices generated by a small deformation linear analysis.
Abstract: Inaccuracies in the length of members and the diameters of joints of large space structures may produce unacceptable levels of surface distortion. Based on the influence matrices generated by a small deformation linear analysis, we formulate a combinatorial optimization problem to minimize surface distortion (DRMS). A deterministic heuristic, tabu search, is presented for DRMS. A comparison of the computational performance of tabu search is made with a randomized heuristic, simulated annealing. Computational experiments are performed on data generated from a two-ring, flat, tetrahedral truss structure.


Journal ArticleDOI
TL;DR: In this paper, a direct search algorithm based on the tabu search methodology is presented, which can escape local optima in the search for a global optimim, and it is shown how the algorithm may be used to solve constrained nonlinear optimization problems.
Abstract: A direct search algorithm is presented which is based on the tabu search methodology. It is shown how the algorithm may be used to solve constrained nonlinear optimization problems. The major merit of the presented approach is the ability of the algorithm to escape local optima in the search for a global optimim.