On universal search strategies for multi-criteria optimization using weighted sums

—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 multi-criteria quadratic assignment problem benchmarks and prove some of its theoretical properties.

• Type: Journal
• Year: 2011
• Where: CEC
• Authors: Julien Legriel, Scott Cotton, Oded Maler
• Keywords: Artificial Intelligence, CEC 2011, Criteria Optimization, Quadratic Assignment Problem, Search Algorithm