October 2000
Intermediate to advanced
288 pages
9h 22m
English
book
Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
by Albert Y. Zomaya, Fikret Ercal, Stephan Olariu
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3.4 A GRAMMAR FOR EXTENDED HYBRIDIZATION SCHEMES
Given a set of optimization algorithms Ai, we have presented a classification of basic hybridizations, which can be described by the following notation:
- LRH (A1(A2)) (homogeneous, heterogeneous) (partial, global) (specialist, general)
- HRH (A1 + A2) (homogeneous, heterogeneous) (partial, global) specialist, general)
- LCH (A1(A2)) (homogeneous, heterogeneous) (partial, global) (specialist, general)
- HCH (A1, A2) (homogeneous, heterogeneous) (partial, global) (specialist, general)
The different metaheuristics Ai treated in our examples are SA (simulated annealing) [39], GA (genetic algorithms) [35], ES (evolution strategies [56], EP (evolutionary programming), GP (genetic programming) [41], LS (descent local search) [54], TS (tabu search) [32], GH (greedy heuristic) [43], AC (ant colonies) [17], SS (scatter search) [31].
These hybridizations should be regarded as primitives that can be combined in different ways. The grammar given in (Fig. 3.10 generalizes the basic hybridization schemes.
Boese et al. [8] suggested an adaptive multistart (AMS) approach, which may be seen as an HRH(LS + LCH(GA(LS))) scheme. First, AMS generates a set of random starting solutions and runs an LS algorithm for each solution to find corresponding local optima. AMS then generates new starting solutions by combining features of the T best local optima seen so far, with T a parameter of the approach. This mechanism bears some resemblance to GAs, but differs ...
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