Name
Abstract | ||
|---|---|---|
In recent years, there have been significant advances in the theory and application of metaheuristics to approximate solutions
of complex optimization Problems. A metaheuristic is an iterative master process that guides and modifies the operations of
subordinate heuristics to efficiently produce high quality solutions, [6] [8]. It may manipulate a complete (or incomplete)
Single Solution or a collection of solutions at each iteration. The subordinate heuristics may be high (or low) level procedures,
or a simple local search, or just a construction method. The family of metaheuristics includes, but is not limited to, Adaptive
memory programming, Ants Systems, Evolutionary methods, Genetic algorithms, Greedy randomised adaptive search procedures,
Neural networks, Simulated annealing, Scatter search, Tabu search and their hybrids.
|
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/978-3-540-48765-4_3 | IEA/AIE |
Keywords | Field | DocType |
unified-metaheuristic framework,tabu search,optimization problem,local search,genetic algorithm,neural network,simulated annealing | Simulated annealing,Mathematical optimization,Computer science,Combinatorial optimization,Heuristics,Artificial intelligence,Local search (optimization),Optimization problem,Tabu search,Machine learning,Genetic algorithm,Metaheuristic | Conference |
Volume | ISSN | ISBN |
1611 | 0302-9743 | 3-540-66076-3 |
Citations | PageRank | References |
1 | 0.42 | 2 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ibrahim H. Osman | 1 | 815 | 94.23 |