Bridging the gap between exact methods and heuristics for multi-objective search

Bilel Derbel — Univ Lille, France

José R. Figueira — Univ Lisbon, Portugal

Carlos M. Fonseca — Univ Coimbra, Portugal

Alexandre D. Jesus — Univ Coimbra, Portugal & Univ Lille, France

Arnaud Liefooghe — Univ Lille, France

Luís Paquete — Univ Coimbra, Portugal

Geoffrey Pruvost — Univ Lille, France

Sébastien Verel — Univ Littoral Côte d’Opale, France

Many real-life applications can be modeled as combinatorial optimization problems with several objectives. Depending of the time available, these problems can be solved by exact or heuristic approaches. Despite the advances on these two solution methods, there is currently little understanding on what they have in common and how they can be combined to solve these problems in a more effective manner. The MOCO-Search project aims to fill this gap. The goal is to establish the link between the design principles of exact and heuristic methods, to identify features that make a problem more difficult to be solved by each method, and to improve their performance by hybridizing search strategies. Special emphasis is given to rigorous performance assessment, benchmarking, and general-purpose guidelines for the design of exact and heuristic multi-objective search.

• October 2019 — Univ Coimbra, Portugal

• June 2019 — Univ Lille, France

• April 2019 — Univ Lille, France

• October 2018 — Univ Coimbra, Portugal — Kick-off Meeting

2021

• Alexandre D. Jesus, Luís Paquete, Arnaud Liefooghe. A model of anytime algorithm performance for bi-objective optimization. Journal of Global Optimization, vol 79, n 1, pp 329–350, 2021

• Alexandre D. Jesus, Luís Paquete, Bilel Derbel, Arnaud Liefooghe. On the design and anytime performance of indicator-based branch and bound for multi-objective combinatorial optimization. Genetic and Evolutionary Computation Conference (GECCO 2021), Lille, France, 2021

2020

• Alexandre D. Jesus, Arnaud Liefooghe, Bilel Derbel, Luís Paquete. Algorithm selection of anytime algorithms. Genetic and Evolutionary Computation Conference (GECCO 2020), pp 850–858, Cancún, Mexico, 2020

2019

• Arnaud Liefooghe, Luís Paquete. Evolutionary computation in combinatorial optimization. Proceedings of the 19th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2019). Lecture Notes in Computer Science vol 11452, Springer, 2019

• Alexandre D. Jesus, Luís Paquete, Arnaud Liefooghe. A model of anytime algorithm performance for biobjective optimization problems. International Workshop on Global Optimization (LeGO 2018), AIP Conference Proceedings 2070, 020049, Leiden, The Netherlands, 2019

2018

• Arnaud Liefooghe, Manuel López-Ibánez, Luís Paquete, Sébastien Verel. Dominance, epsilon, and hypervolume local optimal sets in multi-objective optimization, and how to tell the difference. Genetic and Evolutionary Computation Conference (GECCO 2018), pp 324-331, Kyoto, Japan, 2018

2018

• Luís Paquete. Some results for the hypervolume subset selection problem. Workshop on Recent advances in multi-objective optimization (RAMOO 2018), Nantes, France, Nov 2018

• imappnio — COST action on improving the applicability of nature-inspired optimization by joining theory and practice (CA15140, 2016-2020)

• eaf — empirical attainment function tools

• nasf4nio — not another software framework for nature-inspired optimization

• paradiseo — a software framework for the design of metaheuristics