Last edited by Kagaramar
Friday, January 31, 2020 | History

2 edition of Metaheuristic search concepts found in the catalog.

Metaheuristic search concepts

Günther Zäpfel

Metaheuristic search concepts

a tutorial with applications to production and logistics

by Günther Zäpfel

  • 99 Want to read
  • 18 Currently reading

Published by Springer in Heidelberg, New York .
Written in English

    Subjects:
  • Production management,
  • Metaheuristik,
  • Tourenplanung,
  • Prozessmanagement,
  • Combinatorial optimization,
  • Business logistics,
  • Rucksackproblem,
  • Data processing,
  • Diskrete Optimierung,
  • Logistik

  • Edition Notes

    Includes bibliographical references and index.

    StatementGünther Zäpfel, Roland Braune, Michael Bögl
    ContributionsBraune, Roland, Bögl, Michael
    Classifications
    LC ClassificationsTS155.6 .Z37 2010
    The Physical Object
    Paginationx, 316 p. :
    Number of Pages316
    ID Numbers
    Open LibraryOL25337805M
    ISBN 103642113427, 3642113435
    ISBN 109783642113420, 9783642113437
    LC Control Number2010921288
    OCLC/WorldCa503649530

    Metropolis, A. Various types of memory structures are commonly used to remember specific properties of the trajectory through the search space that the algorithm has undertaken. Category: Mathematics. Such metaheuristics include simulated annealingtabu searchiterated local searchvariable neighborhood searchand GRASP. Haddad O. The goal is to efficiently explore the search space in order to find near—optimal solutions.

    Such metaheuristics include simulated annealingtabu searchiterated local searchvariable neighborhood searchand GRASP. De Causmaecker, S. The authors would advise to start with a simple local search or constructive heuristic and add complexity a perturbation move, a tabu list, another local search operator, In general, metaheuristics frameworks rely heavily on the use of randomness, although some completely deterministic strategies have also been proposed. Three fundamental classes of metaheuristics can be distinguished, based on the way in which solutions are manipulated.

    Moritz and E. To exploit this fact, some metaheuristics define different move types and change the move type used once a local optimum has been reached. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. A Brief History Many problem-solving processes tend to be heuristic throughout the human history; however heuristic as a scientific method for optimization is a modern phenomenon. The most important members of this class are evolutionary algorithms EA because they mimick the principles of natural evolution. Moreover, metaheuristics are more flexible than exact methods in two important ways.


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Metaheuristic search concepts by Günther Zäpfel Download PDF Ebook

A simple strategy such as hill-climbing with random restarts can turn a local search algorithm into an algorithm with global search capability. Holland was the first to use crossover, recombination, mutation and selection in the study of adaptive and artificial systems.

Many variants have been developed over the last two decades, including the clonal selection algorithm, negative selection algorithm, immune networks and others.

Metaheuristic search concepts book essence, randomization is an efficient component for global search algorithms. A third strategy to find good solutions is to use information on the past progress of the search and record this information in memory structures.

Especially for complicated problems or large problem instances, metaheuristics are often able to offer a better trade-off between solution quality and computing time.

Sevaux, and P. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Compared to optimization algorithms and iterative methods, metaheuristics do not guarantee that a globally optimal solution can be found on some class of problems.

Fundamentals of scatter search and path relinking. Both components of a hybrid metaheuristic may run concurrently and exchange information to guide the search. Genetic algorithms in search, optimization, and machine learning.

Essentials of Metaheuristics

Local search metaheuristics Local search LS Metaheuristic search concepts book, or iterative improvement, finds good solutions by iteratively making changes to a single solution, called the current or incumbent solution. Springer, New York, The metaheuristic called guided local search GLS Voudouris and Tsang, introduces a different type of memory, called an augmented objective function, that includes a penalty factor for each potential element.

A common search strategy is the steepest descent or steepest ascent strategy, in which the best move from the neighborhood is selected. Diversification means to generate diverse solutions so as to explore the search space on a global scale, while intensification means to focus the search in a local region knowing that a current good solution is found in this region.

Metaheuristics and exact methods Algorithmic developments in both metaheuristics and exact methods have recently drawn the two fields closely together, and combinations of metaheuristic components usually local search with exact methods for mixed integer linear programming are now common.

Metaheuristic Search Concepts

Figure 2: Genetic algorithm initial population and locations Figure 3: Genetic algorithm Metaheuristic search concepts book locations Differential Evolution Differential evolution DE was developed by R. Joong, and G. Resende, and F.

Evolutionary algorithms operate on a set or population of solutions and use two mechanisms to search for good solutions: the selection of predominantly high-quality solutions from the population and the recombination of those solutions into new ones, using specialized operators that combine the Metaheuristic search concepts book of two or more solutions.

Springer, London, Then, the next move is generated and accepted depending on whether it improves or not, possibly with a probability. Ant and bee algorithms are more suitable for discrete and combinatorial optimization and have been applied in a wide range of applications.

When an ant finds a food source, it will mark it with pheromone and also mark the trail to and from it. These guidance approaches are useful in intensification and diversification strategies related to fixing subsets of variables at particular values, and in strategies that use linear programming to generate trial solutions whose variables are induced to receive integer values.

Guided local search and its application to the traveling salesman problem. For example, an alarming bee may release a chemical message pheromone to stimulate attack response in other bees. In the real world, ecological theory suggests that a species with a small population is in real danger of extinction.

As a simple example, an animation of particles is shown in Fig. Many conferences are dedicated exclusively to evolutionary algorithms. If you have any complaints, bug-fixes, or suggestions, send email to metaheuristics cs.

Improved constructive multistart strategies for the quadratic assignment problem using adaptive memory. The memetic algorithm, proposed by P.A metaheuristic is a consistent set of ideas, concepts, and operators to design a heuristic optimization algorithm, that can provide a sufficiently good solution to an optimization problem with incomplete or imperfect information.

ISBN 13: 9780470582466

Modern and emerging power systems, with the growing complexity of distributed and intermittent generation, are an important application for such galisend.com by: 4. Apr 28,  · This book presents efficient metaheuristic algorithms for optimal design of structures.

Many of these algorithms are developed by the Metaheuristic search concepts book and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Metaheuristic search concepts book Echolocation Optimization, Colliding Bodies Optimization, Ray Brand: Springer International Publishing.

With its wide range of everyday problems and solutions, Metaheursitic Applications in Structures and Infrastructures can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheuristics, optimization in civil engineering and computational intelligence.This book presents efficient metaheuristic algorithms for optimal design pdf structures.

Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Author: Ali Kaveh.Apr 10,  · Simple answer: when deterministic methods don’t work well.

First, the bad: Metaheuristic methods (particle swarm, genetic algorithms, etc.) are rarely more efficient than gradient based methods when an explicit equation based model exists.

These m.Apr 28,  · This book presents ebook metaheuristic algorithms for optimal design of ebook. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Brand: Springer International Publishing.