Results 251 to 260 of about 20,865 (294)

Ant colony optimization: a new meta-heuristic

Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 2003
Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. We put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic.
Marco Dorigo, Gianni Di Caro
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Meta Heuristic Algorithm

Metaheuristic algorithms represent a key area of optimization research that generates interest for many academicians and researchers in today's technology landscape. Researchers have been using these methods as a problem-solving approach for many decades when applied to combinatorial optimization problems, which has now grown into a significant ...
Arshiya Begum, Asfia Sabahath
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Parallel Strategies for Meta-Heuristics

2006
We present a state-of-the-art survey of parallel meta-heuristic developments and results, discuss general design and implementation principles that apply to most meta-heuristic classes, instantiate these principles for the three meta-heuristic classes currently most extensively used—genetic methods, simulated annealing, and tabu search, and identify a ...
Teodor Gabriel Crainic, Michel Toulouse
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A Tutorial on Meta-Heuristics for Optimization

2004
Singapore
Roddick, John Francis, Chu, S-C, Shieh, C-S   +2 more
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Heuristic and Meta-Heuristic Optimization

2016
This chapter deals with the fundamentals of the optimization. The concepts of stochastic optimization and how the stochastic optimization is advantageous over the deterministic approaches are described in Sect. 3.2. Heuristic and meta-heuristic optimization techniques are defined in Sect.
Prajna Kunche, K. V. V. S. Reddy
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Meta–heuristics for Portfolio Optimization: Part I — Review of Meta–heuristics

2023
Kyle Erwin, Andries P. Engelbrecht
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Meta-Heuristics and Channel Assignment

2002
Abstract The channel (or frequency) assignment problem CAP (or FAP) arises in various forms and is a computationally hard problem to solve. In the 1960s and 1970s the effort was directed at developing simple greedy methods, heuristic methods (Zoellner and Beall 1977; Box 1978), and in very specialized cases, exact methods.
Stephen Hurley, Derek Smith
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Evolutionary Computation and Meta-heuristics

2020
This chapter presents several methods of evolutionary computation and meta-heuristics. Evolutionary computation is a computation technique that mimics the evolutionary mechanism of life to select, deform, and convolute data structures. Because of its high versatility, its applications are found in various fields.
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Evolutionary and meta-heuristic scheduling

European Journal of Operational Research, 2007
Kay Chen Tan, Edmund K. Burke, Tong Heng Lee   +2 more
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Meta-heuristic local planning

2014
Navigation is a crucial task for mobile robots driving through dynamic environments. Besides the difficulties involved in finding a way from a starting to some goal location, the problem gets more difficult if unknown objects, dynamics of the robotic vehicle and uncertainties from sensor readings have to be taken into account.
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