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| YZLM5160 | Design of Meta-Heuristic Search Algorithm | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Hamdi Tolga KAHRAMAN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To consider the design of meta-heuristic search (MHS) algorithms as a problem. Analysing of MHS process. To examine the requirements of algorithms in search process. To discuss the basic concepts of meta-heuristic search process such as neighborhood search, diversity, local solution traps, global best solution. Chaos theory, levy steps, Fitness-Distance Balance guide selection method, opposition-based learning and distribution methods to investigate the effects of MHS algorithms on search performance. Examine the techniques to dynamically adjust the design parameters of MHS algorithms. To examine the design of search operators in MHS algorithms. To examine the functions of search operators during the search process. To examine the effects of hybrid MHS techniques on search performance of algorithms. Examining of benchmark problems for continuous optimization. Examine constrained engineering test problems. To compare, test and analyze search performance of MHS algorithms. To perform convergence and diversity tests of MHS algorithms. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Model the search process of MHS algorithms. | 1,4,6 | 1,6, | | PO - 2 : | Explain the basic concepts of MHS algorithms. | 1,4,6 | 1,6, | | PO - 3 : | Discuss the effects of chaos theory, levy steps, Fitness-Distance Balance guide selection method, opposition-based learning and distribution methods on search performance of MHS algorithms. | 1,4,6 | 1,6, | | PO - 4 : | Explain the functions of search operators in MHS algorithms. | 1,4,6 | 1,6, | | PO - 5 : | Develop a new MHS technique or hybrid MHS techniques or variations of existing MHS algorithms. | 1,4,6 | 1,6, | | PO - 6 : | Test and analyze MHS algorithms | 1,4,6 | 1,6, | | PO - 7 : | Compare and analyze search performance of MHS algorithms. | 1,4,6 | 1,6, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Meta-heuristic search (MHS) process, MHS terminology, classical and modern MHS techniques, MHS algorithm design, Chaos theory, Levy steps, Nelder-Mead search method, contrast-based learning, gauss and random distribution methods, search operators, test and comparison problems, constrained optimization engineering problems, development of hybrid MHS algorithms, testing, comparing and analyzing MHS algorithms for search performance. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Meta-heuristic search process and concepts | | | Week 2 | Population-based MSA techniques (GA, PSO, ABC, GSA, CS, SOS, LSA), research paper and application project | | | Week 3 | MHS algorithm design | | | Week 4 | Selection techniques, neighborhood search, diversity and GA/ABC/SOS applications | | | Week 5 | Chaos theory and SOS application | | | Week 6 | Distribution techniques and CS/LSA application | | | Week 7 | Opposition-based learning and SOS example | | | Week 8 | Levy steps and PSO example | | | Week 9 | homework presentation | | | Week 10 | FDB selection method and meta-heuristic search algorithm implementation examples | | | Week 11 | Hybridizing MHS techniques | | | Week 12 | Test problems and constrained engineering problems, Algorithm analysis | | | Week 13 | Project Presentation | | | Week 14 | Project Presentation | | | Week 15 | Project Presentation | | | Week 16 | Final Exam | | | |
| 1 | Talbi, E. G. (2009). Metaheuristics: from design to implementation (Vol. 74). John Wiley & Sons. | | | 2 | Lones, Michael. "Sean Luke: essentials of metaheuristics." (2011): 333-334. (ISBN: 978-1-300-54962-8) | | | 3 | Glover, F. W., & Kochenberger, G. A. (Eds.). (2006). Handbook of metaheuristics (Vol. 57). Springer Science & Business Media. | | | |
| 1 | Yang, X. S. (2010). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons. | | | 2 | Kahraman, H. T., Aras, S., & Gedikli, E. (2020). Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowledge-Based Systems, 190, 105169. | | | 3 | Kahraman, H. T., Bakir, H., Duman, S., Katı, M., Aras, S., & Guvenc, U. (2021). Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination. Applied Intelligence, 1-36. | | | 4 | Aras, S., Gedikli, E., & Kahraman, H. T. (2021). A novel stochastic fractal search algorithm with fitness-Distance balance for global numerical optimization. Swarm and Evolutionary Computation, 61, 100821. | | | 5 | Duman, S., Kahraman, H. T., Guvenc, U., & Aras, S. (2021). Development of a Lévy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems. Soft Computing, 25(8), 6577-6617. | | | 6 | Yang X. S. (2008). Nature-Inspired Metaheuristic Algorithms, Luniver Press, UK. | | | 7 | Kahraman, H. T., Aras, S., Guvenc, U., & Sonmez, Y. (2017, October). Exploring the effect of distribution methods on meta-heuristic searching process. In Computer Science and Engineering (UBMK), 2017 International Conference on (pp. 371-376). IEEE. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 24/11/2021 | 180 | 20 | | Project | 16 | 19/01/2022 | 180 | 30 | | End-of-term exam | 15 | 22/12/2024 | 180 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 5 | 14 | 70 | | Arasınav için hazırlık | 5 | 2 | 10 | | Arasınav | 3 | 1 | 3 | | Uygulama | 1 | 5 | 5 | | Ödev | 2 | 9 | 18 | | Proje | 2 | 14 | 28 | | Kısa sınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 5 | 2 | 10 | | Dönem sonu sınavı | 3 | 1 | 3 | | Total work load | | | 191 |
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