Perturbed Decomposition Algorithm applied to the multi-objective Traveling Salesman Problem

We first prove that the minimum and maximum traveling salesman prob- lems, their metric versions as well as some versions defined on parameterized triangle inequalities

It is our hope that our empirical study can enlighten our current understandings of decomposition-based approaches for multi-objective combinatorial optimization, and can stimulate

First, we propose to augment the single-objective local search that solves the scalarisations with an archive of nondominated solutions, such that they are able to return more than

We address the problem of derivative-free multi-objective optimization of real-valued functions subject to multiple inequality constraints.. The search domain X is assumed to

Genetic algorithms are a revolutionary technique which used operators like mutation, crossover and selection in resolving optimization problems.. They have been used

(Several partial orderings in I(R) exist in literature (see [5, 7]). The results of this paper are based on the partial ordering in the parametric form. However, similar theory may

For the balanced assignment problem and the balanced subset problem, the result tables are similar to the balanced TSP (i.e. time to optimal with the lifted formulation and the

Basically, the new surrogate model referred to as RASM (Rank-based ASM) is only required to locally approximate the Pareto dominance relation, enabling to rank neighbor points