Scatter search

Glover, F., Laguna, M. and Marti, R. (2003) Scatter search, in Advances in Evolutionary Computation Theory and Applications, (eds A. Ghosh and S. Tsut-sui), Springer, New York, pp. 519-537. 

Steps 4 through 6 are the scatter search counterparts to the crossover and mutation operators in genetic algorithms, and the reference set corresponds to the GA... 

Scatter search has been implemented in software called OPTQUEST (see www.opttek.com). OPTQUEST is available as a callable library written in C, which can be invoked from any C program, or as a dynamic linked library (DLL), which can be called from a variety of languages including C, Visual Basic, and Java. The callable library consists of a set of functions that (1) input the problem size and data, (2) set options and tolerances, (3) perform steps 1 through 3 to create an initial reference set, (4) retrieve a trial solution from OPTQUEST to be input to the improvement method, and (5) input the solution resulting from the improvement method back into OPTQUEST, which uses it as the input to step 7 of the scatter search protocol. The improvement method is provided by the user. We use the term improvement loosely here because the user can simply provide an evaluation of the objective and constraint functions. 

Genetic algorithms Scatter search Simulated annealing Tabu search... 

Glover, F. A Template for Scatter Search and Path Relinking, working paper, School of Business, University of Colorado, Boulder, CO, 80309 (1998). 

Using the Evolutionary Algorithm in the Premium Excel Solver / 10.5.6 Scatter Search... 

Cavique L, Rego C, Themido I. A scatter search algorithm for the maximum clique problem, http //hces.bus.olemiss.edu/reports/hces0101.pdf (Accessed on Feb. 28, 2002). 

As an alternative to RSM, simulation responses can be used directly to explore the sample space of control variables. To do so, a lot of combinatorial optimization approaches were adapted for simulation optimization. In general, there are four main classes of methods that have shown a particular applicability in (multi-objective) simulation optimization Meta-heuristics, gradient-based procedures, random search, and sample path optimization. Of particular interest are meta-heuristics as they have shown a good performance for a wide range of combinatorial optimization approaches. Therefore, commercial simulation software primarily uses these techniques to incorporate simulation optimization routines. Among meta-heuristics, tabu search, scatter search, and genetic algorithms are most widely used. Table 4.13 provides an overview on aU aforementioned techniques. 

Beausoleil R. R, 2006. MOSS multiobjective scatter search applied to non-linear multiple criteria optimization,... 

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