Course Overview: Heuristics and Metaheuristics in Operations Research
Explore the practical issues, methods of assessment, recommended textbooks, course catalogue description, aims, and objectives of the course taught by Asst. Prof. Dr. Ahmet NVEREN on Heuristics and Metaheuristics. The course delves into various heuristic methods, metaheuristics, and optimization techniques used in artificial intelligence and operations research to solve complex engineering optimization problems efficiently.
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Asst. Prof. Dr. Ahmet NVEREN 2020-2021 FALL Dr. nveren 1
Practical Issues The lecturer Asst. Prof. Dr. Ahmet NVEREN E-mail: ahmet.unveren@emu.edu.tr CMPE201 Web-pages : cmpe.emu.edu.tr Dr. nveren 2
Practical Issues METHOD OF ASSESSMENT Midterm (1 ) 30 % Assignments (2) + Presentation (1) 30 % Final 40 % Dr. nveren 3
TEXTBOOKs Colin Reeves, Modern Heuristic Techniques for Combinatorial Optimization , John Wiley & Sons, 1993. Judea Pearl, Heuristics: Intelligent Search Strategies for Computer Problem Solving , Addison-Wesley, 1985. Jason Brownlee, Clever Algorithms: Nature-Inspired Programming , 2011. Thomas Back, Evolutionary Algorithms in Theory and Practice , Oxford University Press, 1996. Lecture Notes. Dr. nveren 4
CATALOGUE DESCRIPTION Heuristics and metaheuristics, neighborhood search, local and global optimization, tabu search, greedy randomized adaptive search, simulated annealing, gread deluge algorithm evolutionary algorithms, ant-colony optimization, Particle Swarm Optimization, Bee Colony Optimization hybrid methods, performance evaluation of metaheuristics. Dr. nveren 5
AIMS & OBJECTIVES Heuristics, popularly known as rules of thumb, stand for strategies that improve the average-case performance of problem solving task. An efficient heuristic discovers good solutions for hard problems relatively quickly. Metaheuristics means heuristics for managing heuristics. Metaheuristics control the application and interaction of one or more heuristics searching for a better solution than any single heuristic would find on its own. The aim of this course is to present the nature and the power of widely used metaheuristic methods, primarily those used in artificial intelligence and operations research. The methods to be covered are used to solve search, reasoning, planning and general engineering optimization problems. The graduate students who will take this course may use many of the algorithms introduced in this course in their graduate research studies. Dr. nveren 6
