Scatter Search

Abstract

The evolutionary approach called scatter search originated from strategies for creating composite decision rules and surrogate constraints. Recent studies demonstrate the practical advantages of this approach for solving a diverse array of optimisation problems from both classical and real—world settings. Scatter search contrasts with other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalised path constructions in Euclidean space and by utilising strategic designs where other approaches resort to randomisation. Additional advantages are provided by intensification and diversification mechanisms that exploit adaptive memory, drawing on foundations that link scatter search to tabu search. The main goal of this chapter is to demonstrate the development of a scatter search procedure by demonstrating how it may be applied to a class of non-linear optimisation problems on bounded variables. We conclude the chapter by highlighting key ideas and research issues that offer the promise of yielding future advances.