We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Autonomous Parameter Balance in Population-Based Approaches: A Self-Adaptive Learning-Based Strategy.
- Authors
Vega, Emanuel; Lemus-Romani, José; Soto, Ricardo; Crawford, Broderick; Löffler, Christoffer; Peña, Javier; Talbi, El-Gazhali
- Abstract
Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process.
- Subjects
SELF-adaptive software; METAHEURISTIC algorithms; MANUFACTURING cells; KNAPSACK problems; ALGORITHMS
- Publication
Biomimetics (2313-7673), 2024, Vol 9, Issue 2, p82
- ISSN
2313-7673
- Publication type
Article
- DOI
10.3390/biomimetics9020082