Iterated Local Search
H. R. Lourenco, O. C. Martin, T. Stutzle
Abstract
This is a survey of "Iterated Local Search", a general purpose metaheuristic for finding good solutions of combinatorial optimization problems. It is based on building a sequence of (locally optimal) solutions by: (1) perturbing the current solution; (2) applying local search to that modified solution. At a high level, the method is simple, yet it allows for a detailed use of problem-specific properties. After giving a general framework, we cover the uses of Iterated Local Search on a number of well studied problems.
Create a lesson
Related papers
A Network Simplex Framework For Discrete Unbalanced Optimal Transport
Gaetano Agazzotti, Antonin Chambolle, Clément W. Royer
Warm-IP: A Path-Following ADMM Warm Start for Interior-Point Quadratic Programming
Babak Aslani, Mojtaba Tefagh, Gourav Jhanwar et al.
Adaptive Variance Reduction without Knowing Oracle Smoothness
Wei Jiang, Yibo Wang, Zechao Li
Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints
Gon Buzaglo, Guy Kornowski
Geometry-Aware Multi-Objective Optimization: Norm-Constrained Oracles with Entropic Weight Updates
El Mahdi Chayti, Alireza Mirrokni, Martin Jaggi
Estimation, Sensitivity Analysis, and Optimization of Distortion Risk Measures via Simulation
Hao Cao, Jian-Qiang Hu, Jiaqiao Hu