Lessons from Applying and Experimenting with Scatter Search

Abstract

Scatter search is an evolutionary method that has been successfully applied to hard optimization problems. The fundamental concepts and principles of the method were first proposed in the 1970s and were based on formulations, dating back to the 1960s, for combining decision rules and problem constraints. The method uses strategies for search diversification and intensification that have proved effective in a variety of optimization problems. This paper presents a number of findings (lessons) from the application of scatter search to combinatorial optimization problems (e.g., production scheduling and the linear ordering problem) and nonlinear optimization problems (e.g., multi-modal functions and neural network training). We describe our findings and the context in which we have learned each lesson. We believe that some of our findings are not context specific and therefore may benefit future applications of scatter search.KeywordsScatter SearchMetaheuristicsOptimization