Gray-Box Tunneling for Multi-Objective Combinatorial Optimization

Inria, the French national research institute for the digital sciences; France; Sur place; EUR 7 800 - 10 000; Plein temps

Avantages offerts par ce poste

Public transport reimbursement

Annual leave 7 weeks + 10 RTT days

Teleworking after 6 months

Flexible hours

Equipment provided

Social, cultural and sports events

Vocational training access

Social security coverage

Résumé du poste

Inria, the French national research institute for the digital sciences in France, seeks a researcher for a temporary internship focused on multi-objective gray-box optimization. The project aims to develop new algorithmic approaches to navigate Pareto Local Optima and explore tunneling mechanisms within gray-box problems.

The work may involve theoretical analysis, algorithm design, experimental evaluation, or combined methods, with potential contributions to publications and research software.

Qualifications

• Strong interest in fundamental research in gray-box optimization.
• Good programming skills are expected.
• Willingness to conduct open research problems.
• Ability to contribute to scientific publications or research software.

Responsabilités

• Review literature on gray-box optimization and multi-objective optimization.
• Analyze existing gray-box optimization algorithms and their landscapes.
• Design new tunneling operators and search mechanisms.
• Implement and experimentally evaluate new optimization algorithms and run computational experiments.

Connaissances

Programming skills

French (basic)

English (good)

Description du poste

Inria, the French national research institute for the digital sciences

Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile First Stage Researcher (R1) Application Deadline 31 Dec 2026 - 00:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 38.5 Offer Starting Date 1 Mar 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 2026-10556 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Multi-objective combinatorial optimizationaims at simultaneously optimizing several, potentially conflicting, objective functions over a discrete decision space. In many combinatorial optimization problems, objective functions exhibit a gray-box structure,e.g.,they can be decomposed into sub-functions involving only a restricted number of decision variables. Such structure can be exploited to design specialized and computationally efficient optimization algorithms.

For example, for many binary optimization problems, objective functions can be represented using a bounded-degree Walsh/Fourrier transform, providing a natural framework for analyzing and exploiting variable interactions.Other combinatorial problem representations may also be considered, provided that they expose a suitable structure that can be leveraged by specialized gray-box evolutionary and search operators.

While gray-box optimization has received increasing attention in the single-objective setting, the study of gray-box multi-objective optimization remains largely unexplored. This internship aims to contribute to this emerging research direction by developing new algorithmic approaches for efficiently navigating the landscape of multi-objective combinatorial optimization problems.

A particular focus will be placed on Pareto Local Optima (PLOs). PLOs generalize the notion of local optima to the multi-objective setting by considering the dominance relation. The general objective of the internship is to develop new tunneling mechanisms that allow optimization algorithms to efficiently navigate between PLO solutions, potentially enabling the discovery of high-quality regions of the Pareto set that are difficult to reach through conventional local search.

Depending on the candidate's background and interests, the work may involve theoretical analysis, algorithm design, experimental evaluation, or a combination of these aspects.

The internship may focus on one or more of the following research directions:

• Analyzing Pareto Local Optima under tunneling mechanisms such as partition crossover. Study the structure and properties of Pareto Local Optima (PLOs) that can be exploited by partition crossover and related gray-box operators.
• Designing new multi-objective tunneling mechanisms. Develop and investigate partition-crossover-based mechanisms to efficiently move between PLOs, with a particular focus on (i) decomposition-based multi-objective optimization and cooperation between neighboring subproblems and/or (ii) Dominance based techniques.
• Integrating tunneling into (HPC-)accelerated multi-objective search algorithms. Incorporate partition crossover and related tunneling mechanisms into specialized evolutionary and local-search algorithms, especially by exploiting parallel and high-performance computing to accelerate the search.

Consequently, the internship may involve:

• reviewing the literature on gray-box optimization, multi-objective optimization, local search, and Pareto Local Optima
• analyzing existing gray-box optimisation algorithms
• conducting theoretical or empirical analyses of fitness landscapes and PLO structures
• designing new tunneling operators and search mechanisms
• implementing and experimentally evaluating new optimization algorithms
• designing computational experiments and analyzing algorithmic performance
• exploiting parallel and HPC computing when relevant
• contributing to scientific publications and/or research software.

The internship is intended as a research-oriented project, and the research questions and methodology will be refined continously accordingto theresults obtained during the internship.

A strong interest in conducting fundamental research and exploring open-ended applied algorithmic questions is particularly welcome.Good programming skills are expected.

Specific Requirements

The internship will involve both fundamental and applied research in gray-box multi-objective optimization. The candidate will have the opportunity to work on open research problems, contribute to the development of new optimization algorithms, and potentially contribute to scientific publications resulting from the work.

Languages FRENCH Level Basic

Languages ENGLISH Level Good

Additional Information

• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

According to current regulations: €4.50 per hour