The aim of the course is to introduce students to the basic principles of biologically inspired optimization, as well as to provide them with theoretical and practical knowledge and skills so that they would be able to develop and implement optimization algorithms for solving engineering problems.

After successfully completing this course, the students should be able to: - formulate and mathematically model the optimization problem; - understand all the phases necessary for algorithm implementation; - implement the algorithm with the objective to minimize/maximize the fitness function according to optimization criteria; - develop their own codes in MATLAB environment and experimentally evaluate the performance of the algorithm; - carry out scientific research work and apply biologically inspired algorithms to solve real-world optimization problems.

Introduction to biologically inspired optimization algorithms. Discrete and continuous optimization problems. Combinatorial optimization problems. NP-hard problems. Stochastic optimization. Single-objective and multi-objective optimization. Review of optimization algorithms. Simulated Annealing. Tabu Search algorithm (TS). Evolutionary metaheuristic algorithm: Genetic Algorithms (GA), Evolutionary Programming (EP), Evolution Strategy (ES), Genetic Programming (GP). Swarm Intelligence: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Ant Lion Optimizer (ALO), Bee Colony Optimization (BCO), Firefly Algorithm (FA), Fruit fly Optimization Algorithm (FOA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), etc. Hybrid metaheuristic algorithms. Chaos theory and optimization algorithms. The basic concepts of biologically inspired optimization algorithms, solution coding/decoding procedures, operators, parameters settings, and parameters tuning. The performance of optimization algorithms. Comparison of optimization algorithms.

Research on biologically inspired optimization algorithms. Implementation of biologically inspired methods for solving practical optimization problems, depending on the candidate’s doctoral thesis. Laboratory work. Writing a seminar work. Publication of a research paper.

Completed technical college with basic programming knowledge in MATLAB environment.

Laboratory for industrial robotics and artificial intelligence (ROBOTICS&AI), Department of Production Engineering, University of Belgrade - Faculty of Mechanical Engineering. MATLAB software.

Total assigned hours: 65

New material: 30
Elaboration and examples (recapitulation): 20

Auditory exercises: 0
Laboratory exercises: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 10
Review and grading of the project: 0
Test: 0
Test: 0
Final exam: 5

Activity during lectures: 0
Test/test: 0
Laboratory practice: 0
Calculation tasks: 0
Seminar paper: 70
Project: 0
Final exam: 30
Requirement for taking the exam (required number of points): 50

Engelbrecht, A.P. (2007). Computational intelligence: an introduction. John Wiley & Sons. ISBN 978-0-470-03561-0 ; Talbi, E.G. (2009). Metaheuristics: from design to implementation (Vol. 74). John Wiley & Sons. ISBN 978-0-470-27858-1; Wahde, M. (2008). Biologically inspired optimization methods: an introduction. WIT press. ISBN 978-1-84564-148-1; Yu, X., Gen, M. (2010). Introduction to evolutionary algorithms. Springer Science & Business Media. ISBN 978-1-84996-128-8; Yang, X.S. (2010). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons. ISBN 978-0-470-58246-6