Optimization algorithms, benchmarks and performance measures: From static to dynamic environment

Abstract

This paper is a tentative to describe the basics of dynamic optimization using swarm & evolutionary methods. Computational intelligence methods based on swarming, collaborative computing and related techniques showed their potentials at solving classical static problems; for dynamic problems new paradigms needs to be established, this concerns the methods, the test benches and the performance evaluation processes. A review of the key population based computational techniques is performed prior to set some perspective guidelines on how to handle the multi-objective dynamic problems using these technique.