Performance Measures for Multi-objective Optimization Algorithms
Elena Simona · 2007
In the real world, the most optimization problems are multi-objective. For the complex ones we do not often have polynomial algorithms which return the exact solution(s) in practical time. For this reason, we use approximation algorithms which find solution(s) (near) optimal in a practical time. Alongside the aspects related to comparing two multi-objective solutions, there are also aspects related to measuring the performance of an algorithm. In the paper we present the most important performance measures for multi-objective optimization algorithms. As a practical example, we have compared three genetic algorithms on the bi-objective JSSP test-problem ft10, and the results showed the necessity to simultaneously consider many performance measures for the multi-objective algorithms.