Most engineering problems of interest use complex algorithms, and use up considerable computer resources (time, space, the number of processors). When lacking the efficient deterministic and approximative algorithms for solving complex problems, adequately designed and applied (meta)heuristcs produce acceptable (suboptimal) solutions in an acceptable time frame. The educational objective of the course is to offer a well organized and comprehensive comparative overview of (meta)heuristics and soft-computing techniques widley used in practical engineering solutions of difficult algorithm problems.

Students who complete the course: -understand basic (meta)heuristics and soft-computing techniques for algorithm problem solving, -develop the ability to classify problems (to determine the level of algorithm difficulty of the problem, to reduce problem to the existing problem types), -can work with different programming libraries that use (meta)heuristics of general and specific applicaition

Types of algorithms: deterministic, approximative, randomized, heuristic and metaheuristic; why and when to use (meta)heuristics. Traditional deterministic searching methods. Simple heuristic methods: types of heuristics, heuristic design, local search heuristics, heuristics based on local search, interative local search. Metaheuristics: evolutionary computation (EC), evolutionary algorithms (EA), evolutionary strategies (ES), evolutionary programming (EP), genetic algorithms (GA), genetic programming (GP), hybrid methods; tabu search (TS), simulated annealing (SA), quantum annealing (QA), ant colony optimization (ACO), swarm intellience (SI), memetic algorithms (MA). Soft-computing: artificial neural networks (ANN), cell neural networks (CNN), fuzza logic based algorithms (FA), hybrid methods (neuro-fuzzy, fuzzy-genetic, etc.). The use of heuristics, metaheuristics and soft-computing in algorithm solutions to difficult (optimization) engineering problems, such as linear programming (LP), integral programming (IP), 0-1 integral programming (0-1 IP), non-linear programming (NLP), single objective (SO) and multi-objective (MO) optimization goals.

Lectures. Auditory practice. Computer practice. Laboratory practice. Tutorial work.