Reducing Local Optima in Single-Objective Problems by Multi-objectivization

One common characterization of how simple hill-climbing optimization methods can fail is that they become trapped in local optima - a state where no small modi cation of the current best solution will produce a solution that is better. This measure of `better' depends on the performance of the solution with respect to the single objective being optimized. In contrast, multi-objective optimization (MOO) involves the simultaneous optimization of a number of objectives. Accordingly, the multi-objective notion of `better' permits consideration of solutions that may be superior in one objective but not in another. Intuitively, we may say that this gives a hill-climber in multi-objective space more freedom to explore and less likelihood of becoming trapped. In this paper, we investigate this intuition by comparing the performance of simple hill-climber-style algorithms on single-objective problems and multiobjective versions of those same problems. Using an abstract buildingblock p...

• Type: Conference
• Year: 2001
• Where: EMO
• Authors: Joshua D. Knowles, Richard A. Watson, David Corne
• Keywords: EMO 2001, Local Optima, Multi-objective Optimization, Optimization, Simple Hill-climbing Optimization