On Universal Search Strategies for Multi-Criteria Optimization

We develop a stochastic local search algorithm for finding Pareto points for multi-criteria optimization problems. The algorithm alternates between different single-criterium optimization problems characterized by weight vectors. The policy for switching between different weights is an adaptation of the universal restart strategy defined by [LSZ93] in the context of Las Vegas algorithms. We demonstrate the effectiveness of our algorithm on multicriteria quadratic assignment problem benchmarks and prove some of its theoretical properties.