About the lab

Our lab is focused on the development and deployment of novel artificial intelligence algorithms in the following areas:
- Evolutionary computation
- Swarm intelligence
- Artificial neural networks
- Chaos theory
- Complex systems
- Data mining
- Image processing
- Pattern recognition
- Dynamic data prediction

Featured research (5)

Comparing various metaheuristics based on an equal number of objective function evaluations has become standard practice. Many contemporary publications use a specific number of objective function evaluations by the benchmarking sets definitions. Furthermore, many publications deal with the recurrent theme of late stagnation, which may lead to the impression that continuing the optimization process could be a waste of computational capabilities. But is it? Recently, many challenges, issues, and questions have been raised regarding fair comparisons and recommendations towards good practices for benchmarking metaheuristic algorithms. The aim of this work is not to compare the performance of several well-known algorithms but to investigate the issues that can appear in benchmarking and comparisons of metaheuristics performance (no matter what the problem is). This paper studies the impact of a higher evaluation number on a selection of metaheuristic algorithms. We examine the effect of a raised evaluation budget on overall performance, mean convergence, and population diversity of selected swarm algorithms and IEEE CEC competition winners. Even though the final impact varies based on current algorithm selection, it may significantly affect the final verdict of metaheuristics comparison. This work has picked an important benchmarking issue and made extensive analysis, resulting in conclusions and possible recommendations for users working with real engineering optimization problems or researching the metaheuristics algorithms. Especially nowadays, when metaheuristic algorithms are used for increasingly complex optimization problems, and meet machine learning in AutoML frameworks, we conclude that the objective function evaluation budget should be considered another vital optimization input variable.
The primary aim of this original work is to provide a more in-depth insight into the relations between control parameters adjustments, learning techniques, inner swarm dynamics and possible hybridization strategies for popular swarm metaheuristic Firefly Algorithm (FA). In this paper, a proven method, orthogonal learning, is fused with FA, specifically with its hybrid modification Firefly Particle Swarm Optimization (FFPSO). The parameters of the proposed Orthogonal Learning Firefly Algorithm are also initially thoroughly explored and tuned. The performance of the developed algorithm is examined and compared with canonical FA and above-mentioned FFPSO. Comparisons have been conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using the Friedman rank test.
Many state-of-the-art optimization algorithms stand against the threat of premature convergence. While some metaheuristics try to avoid it by increasing the diversity in various ways, the Bison Algorithm faces this problem by guaranteeing stable exploitation – exploration ratio throughout the whole optimization process. Still, it is important to ensure, that the newly discovered solutions can affect the overall optimization process. In this paper, we propose a new Run Support Strategy for the Bison Algorithm, that should enhance the utilization of newly discovered solutions, and should be suitable for both continuous and discrete optimization.
Currently, electromagnetic compatibility presents a severe problem for electric and electronic devices; therefore, the demand for protection has rapidly increased in recent years. Unfortunately, the design of a high-quality shield can involve different pitfalls, and it is impossible to explore and test every possible solution. Many times, the model of an existing structure form different scientific areas have been successfully redesigned using knowledge and techniques adopted from the field of artificial intelligence. The soft computing based approach has been verified here, and selected real case study is presented in this paper.

Lab head

Roman Senkerik
Department
  • Department of Informatics and Artificial Intelligence

Members (8)

Michal Pluhacek
  • Tomas Bata University in Zlín
Zuzana Kominkova Oplatkova
  • Tomas Bata University in Zlín
Adam Viktorin
  • Tomas Bata University in Zlín
Tomas Kadavy
  • Tomas Bata University in Zlín
Huy M. Huynh
  • Tomas Bata University in Zlín
Anezka Kazikova
  • Tomas Bata University in Zlín
Alžběta Turečková
  • Tomas Bata University in Zlín
Luis Antonio Beltran Prieto
  • Tomas Bata University in Zlín