Vida Vukašinović

Researchshow all

Randomised Optimisation Algorithms Research Network (2023-2027)
Computer structures and systems (2019-2027)
Fleet and traffic management systems for conducting future cooperative mobility (2022-2025)
Intelligent Secure Trustable Things (2020-2023)
Uncertainty Treatment and OPtimisation In Aerospace Engineering (2017-2020)
Biomedical data fusion by nonnegative matrix tri-factorization (2017-2020)
Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (2016-2020)

Appliedshow all

Kranj’s Resilient Ecosystem for Active Transformation, Innovation, and Visionary Engagement (2025-2026)
Urban Pioneers - Systemic Change Amid Livable Environments (2023-2025)
Implementing gender equality plans to unlock research potential of RPOs and RFOs in Europe (2021-2025)
Social Innovation for integrated health CARE of ageing population in ADRION Regions (2020-2023)

Randomised Optimisation Algorithms Research Network

acronym: ROAR-NET
type: COST
duration: 2023-2027
https://roar-net.eu/

content:
The multiple requirements placed on modern real-world processes and systems are ever more demanding. Meeting such requirements can only be achieved through systematic methods capable of identifying the best course(s) of action among the possible alternatives, which are generally known as optimisation algorithms. Optimisation algorithms find application in virtually all areas of knowledge and human activity, but require suitable models of the problems of interest in order to operate. Producing such models is often a challenging task which involves understanding both the problem at hand and the type of optimisation algorithm to be used, and may entail significant effort. Compared to their deterministic counterparts, randomised optimisation algorithms tend to be simpler to design and implement while offering improved performance, particularly on large problems whose internal structure is not sufficiently well known or even available. However, randomised optimisation algorithms are still far from reaching the level of widespread and systematic adoption enjoyed by more traditional optimisation solvers in the real world. This COST Action aims at making randomised optimisation algorithms widely competitive in practice by identifying and reducing obstacles to their adoption at the scientific, technical, economic, and human levels. It focuses on meeting the needs of practitioners, from whose activities the economic value of optimisation solvers stems. These needs are taken as the driving force for new theoretical, methodological, and technical advances leading to the sustainable development of widely available software tools, training materials and programmes, and ultimately to more extensive acceptance and deployment of these methods.