Swarm Optimization
We study the use of swarm intelligence approaches for optimization. In particular, the goal is to significantly broaden the range of applications of swarm intelligence to include the solution of rich optimization problems, that is, problems that are multi-objective, dynamic, and stochastic.
Computationally difficult optimization problems frequently appear in many important fields. A few examples with high impact are the creation of good schedules and timetables, the design of efficient networks, and the solution of various problems that arise in bioinformatics. Generating high-quality or optimal solutions to these problems can result in enormous economic savings, in a more efficient usage of scarce resources, or in new scientific insights. Most academic research on algorithmic solutions to optimization problems has focused on problems that are strong simplifications of those practically occurring in the real world. This makes the results obtained by academic researchers often difficult to apply to problems that, as is the case in the real-world, present features such as multiple objectives (that is, solutions are evaluated according to several, often conflicting criteria), and dynamic modifications of the data (that is, the objective function or the constraints may change while implementing a solution). Similar difficulties apply to problems where the objective function or the constraints are stochastic in nature. We call problems that have at least one of these features rich optimization problems. We believe that swarm intelligence approaches are particularly well suited for the solution of rich optimization problems, because they already demonstrated a very high performance on the academic simplified instances, and also because they are inherently robust, distributed, flexible and stochastic.
