A modified choice function hyper-heuristic with Boltzmann function
dc.citation.epage | 746 | |
dc.citation.issue | 4 | |
dc.citation.spage | 736 | |
dc.contributor.affiliation | Університет Султана Мулая Слімана | |
dc.contributor.affiliation | University Sultan Moulay Slimane | |
dc.contributor.author | Меллулі, О. | |
dc.contributor.author | Гафіді, І. | |
dc.contributor.author | Метране, А. | |
dc.contributor.author | Mellouli, O. | |
dc.contributor.author | Hafidi, I. | |
dc.contributor.author | Metrane, A. | |
dc.coverage.placename | Львів | |
dc.coverage.placename | Lviv | |
dc.date.accessioned | 2023-11-01T07:49:25Z | |
dc.date.available | 2023-11-01T07:49:25Z | |
dc.date.created | 2021-03-01 | |
dc.date.issued | 2021-03-01 | |
dc.description.abstract | Гіпер-евристика — це підклас методів дослідження високого рівня, які функціонують у просторі евристичних досліджень низького рівня. Їхня мета — покращити рівень загальності для розв’язування задач комбінаторної оптимізації за допомогою двох основних компонентів: методології евристичного вибору та критерію прийнятності ходу для забезпечення інтенсифікації та диверсифікації [1]. Таким чином, замість того, щоб безпосередньо працювати над розв’язками задачі та обирати один з них, щоб перейти до наступного кроку на кожному етапі, гіпер-евристика діє у просторі евристичного дослідження низького рівня. Функція вибору є однією з гіпер-евристик, які довели свою ефективність у розв’язанні задач комбінаторної оптимізації [2–4]. На кожній ітерації вибір евристики залежить від оцінки, обчисленої шляхом поєднання трьох різних показників, щоб гарантувати як інтенсифікацію, так і диверсифікацію процесу вибору евристики. Тому для розв’язуванн задачі вибирається евристика з найвищим балом. Отже, ключем до успіху в виборі функції є вибір правильних вагових параметрів для трьох її мір. У цій роботі виконано сучасне гіперевристичне дослідження та запропоновано новий метод, який автоматично керує цими ваговими параметрами на основі функції Больцмана. Проведено порівняння результатів, отриманих внаслідок його застосування до п’яти предметних областей, з результатами методу стандартної модифікованої функції вибору, які запропоновані Дрейком та ін. [2, 3]. | |
dc.description.abstract | Hyper-heuristics are a subclass of high-level research methods that function in a low-level heuristic research space. Their aim objective is to improve the level of generality for solving combinatorial optimization problems using two main components: a methodology for the heuristic selection and a move acceptance criterion, to ensure intensification and diversification [1]. Thus, rather than working directly on the problem’s solutions and selecting one of them to proceed to the next step at each stage, hyper-heuristics operates on a low-level heuristic research space. The choice function is one of the hyper-heuristics that have proven their efficiency in solving combinatorial optimization problems [2–4]. At each iteration, the selection of heuristics is dependent on a score calculated by combining three different measures to guarantee both intensification and diversification for the heuristics choice process. The heuristic with the highest score is therefore chosen to be applied to the problem. Therefore, the key to the success of the choice function is to choose the correct weight parameters of its three measures. In this study, we make a state of the art in hyper-heuristic research and propose a new method that automatically controls these weight parameters based on the Boltzmann function. The results obtained from its application on five problem domains are compared with those of the standard, modified choice function proposed by Drake et al. [2, 3]. | |
dc.format.extent | 736-746 | |
dc.format.pages | 11 | |
dc.identifier.citation | Mellouli O. A modified choice function hyper-heuristic with Boltzmann function / O. Mellouli, I. Hafidi, A. Metrane // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 4. — P. 736–746. | |
dc.identifier.citationen | Mellouli O. A modified choice function hyper-heuristic with Boltzmann function / O. Mellouli, I. Hafidi, A. Metrane // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 4. — P. 736–746. | |
dc.identifier.doi | 10.23939/mmc2021.04.736 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/60438 | |
dc.language.iso | en | |
dc.publisher | Видавництво Львівської політехніки | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Mathematical Modeling and Computing, 4 (8), 2021 | |
dc.relation.references | [1] Burke E. K., Hyde M., Kendall G., Ochoa G., Ozcan E., Woodward J. R. A classification of hyper-heuristic ¨ approaches in Handbook of Metaheuristics. Springer US. 449–468 (2010). | |
dc.relation.references | [2] Drake J. H., Ozcan E., Burke E. K. An Improved Choice Function Heuristic Selection for Cross Domain ¨ Heuristic Search. PPSN 2012: Parallel Problem Solving from Nature – PPSN XII. 307–316 (2012). | |
dc.relation.references | [3] Drake J. H., Ozcan E., Burke E. K. A Modified Choice Function hyper-heuristic controlling unary and ¨ binary operators. 2015 IEEE Congress on Evolutionary Computation (CEC). 3389–3396 (2015). | |
dc.relation.references | [4] Tay J. C., Ho N. B. Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering. 54 (3), 453–473 (2008). | |
dc.relation.references | [5] Lyaqini S., Nachaoui M., Quafafou M. Non-smooth classification model based on new smoothing technique. Journal of Physics: Conference Series. 1743 (1), 012025 (2021). | |
dc.relation.references | [6] Cowling P. I., Kendall G., Soubeiga E. A Hyperheuristic Approach to Scheduling a Sales Summit. PATAT 2000: Practice and Theory of Automated Timetabling III. 2079, 176–190 (2001). | |
dc.relation.references | [7] Crowston W. B., Glover F., Thompson G. L., Trawick J. D. Probabilistic and parametric learning combinations of local job shop scheduling rules. ONR research memorandum, Carnegie-Mellon University, Pittsburgh (1963). | |
dc.relation.references | [8] Fisher H., Thompson G. L. Probabilistic learning combinations of local job-shop scheduling rules. In: Muth J. F., Thompson G. L. (eds). Industrial Scheduling. 225–251 (1963). | |
dc.relation.references | [9] Fisher H., Thompson G. L. Probabilistic learning combinations of local job-shop scheduling rules. In: Factory Scheduling Conference, Carnegie Institue of Technology. May 10–12 (1961). | |
dc.relation.references | [10] Nachaoui M., Chakib A., Nachaoui A. An efficient evolutionary algorithm for a shape optimization problem. Applied and Computational Mathematics. 19 (2), 220–244 (2020). | |
dc.relation.references | [11] Oteiza P. P., Rodriguez D. A., Brignole N. B. Parallel cooperative optimization through hyper-heuristics. Computer Aided Chemical Engineering. 44, 805–810 (2018). | |
dc.relation.references | [12] Burke E. K., Hyde M., Kendall G., Ochoa G., Ozcan E., Woodward J. Exploring hyper-heuristic methodologies with genetic programming. Computational Intelligence. 177–201 (2009). | |
dc.relation.references | [13] Burke E. K., Hyde M. R., Kendall G., Woodward J. Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation. 1559–1565 (2007). | |
dc.relation.references | [14] Burke E. K., Hyde M. R., Kendall G., Woodward J. R. The scalability of evolved on line bin packing heuristics. 2007 IEEE Congress on Evolutionary Computation. 2530–2537 (2007). | |
dc.relation.references | [15] Burke E. K., Hyde M. R., Kendall G. Evolving bin packing heuristics with genetic programming. Parallel Problem Solving from Nature – PPSN IX. 860–869 (2006). | |
dc.relation.references | [16] Dimopoulos C., Zalzala A. M. S. Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software. 32 (6), 489–498 (2001). | |
dc.relation.references | [17] Fukunaga A. Automated discovery of composite SAT variable selection heuristics. Proceedings of the National Conference on Artificial Intelligence (AAAI). 641–648 (2002). | |
dc.relation.references | [18] Fukunaga A. S. Automated discovery of local search heuristics for satisfiability testing. Evol. Comput. 16 (1), 31–61 (2008). | |
dc.relation.references | [19] Fukunaga A. S. Evolving local search heuristics for SAT using genetic programming. Genetic and Evolutionary Computation – GECCO-2004. 483–494 (2004). | |
dc.relation.references | [20] Geiger C. D., Uzsoy R., Aytug H. Rapid modeling and dis- covery of priority dispatching rules: an autonomous learning approach. Journal of Scheduling. 9, 7–34 (2006). | |
dc.relation.references | [21] Keller R. E., Poli R. Cost-benefit investigation of a genetic-programming hyperheuristic. EA 2007: Artificial Evolution. 13–24 (2007). | |
dc.relation.references | [22] Keller R. E., Poli R. Linear genetic programming of parsimonious metaheuristics. 2007 IEEE Congress on Evolutionary Computation. 4508–4515 (2007). | |
dc.relation.references | [23] Acevedo N., Barra C. R., Bolton C. C., Parada V. Automatic design of specialized algorithms for the binary knapsack problem. Expert Systems with Applications. 141, 112908 (2020). | |
dc.relation.references | [24] Eglese R. W. Simulated annealing: A tool for operational research. European Journal of Operational Research. 46 (3), 271–281 (1990). | |
dc.relation.references | [25] Fleischer M. A. Simulated annealing: Past, present, and future. Proceedings of the 1995 Winter Simulation Conference. 155–161 (1995). | |
dc.relation.references | [26] Koulamas C., Antony S. R., Jaen R. A survey of simulated annealing applications to operations research problems. Omega. 22 (1), 41–56 (1994). | |
dc.relation.references | [27] Kirkpatrick S., Gelatt Jr, C. D., Vecchi M. P. Optimization by Simulated Annealing. Science. 220 (4598), 671–680 (1983). | |
dc.relation.references | [28] Romeo F., Sangiovanni-Vincentelli A. A theoretical frame-work for simulated annealing. Algorithmica. 6, Article number: 302 (1991). | |
dc.relation.references | [29] Suman B., Kumar P. A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society. 57 (10), 1143–1160 (2006). | |
dc.relation.references | [30] Alahyane M., Hakim A., Laghrib A., Raghay S. A lattice Boltzmann method applied to the fluid image registration. Applied Mathematics and Computation. 349, 421–438 (2019). | |
dc.relation.referencesen | [1] Burke E. K., Hyde M., Kendall G., Ochoa G., Ozcan E., Woodward J. R. A classification of hyper-heuristic ¨ approaches in Handbook of Metaheuristics. Springer US. 449–468 (2010). | |
dc.relation.referencesen | [2] Drake J. H., Ozcan E., Burke E. K. An Improved Choice Function Heuristic Selection for Cross Domain ¨ Heuristic Search. PPSN 2012: Parallel Problem Solving from Nature – PPSN XII. 307–316 (2012). | |
dc.relation.referencesen | [3] Drake J. H., Ozcan E., Burke E. K. A Modified Choice Function hyper-heuristic controlling unary and ¨ binary operators. 2015 IEEE Congress on Evolutionary Computation (CEC). 3389–3396 (2015). | |
dc.relation.referencesen | [4] Tay J. C., Ho N. B. Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering. 54 (3), 453–473 (2008). | |
dc.relation.referencesen | [5] Lyaqini S., Nachaoui M., Quafafou M. Non-smooth classification model based on new smoothing technique. Journal of Physics: Conference Series. 1743 (1), 012025 (2021). | |
dc.relation.referencesen | [6] Cowling P. I., Kendall G., Soubeiga E. A Hyperheuristic Approach to Scheduling a Sales Summit. PATAT 2000: Practice and Theory of Automated Timetabling III. 2079, 176–190 (2001). | |
dc.relation.referencesen | [7] Crowston W. B., Glover F., Thompson G. L., Trawick J. D. Probabilistic and parametric learning combinations of local job shop scheduling rules. ONR research memorandum, Carnegie-Mellon University, Pittsburgh (1963). | |
dc.relation.referencesen | [8] Fisher H., Thompson G. L. Probabilistic learning combinations of local job-shop scheduling rules. In: Muth J. F., Thompson G. L. (eds). Industrial Scheduling. 225–251 (1963). | |
dc.relation.referencesen | [9] Fisher H., Thompson G. L. Probabilistic learning combinations of local job-shop scheduling rules. In: Factory Scheduling Conference, Carnegie Institue of Technology. May 10–12 (1961). | |
dc.relation.referencesen | [10] Nachaoui M., Chakib A., Nachaoui A. An efficient evolutionary algorithm for a shape optimization problem. Applied and Computational Mathematics. 19 (2), 220–244 (2020). | |
dc.relation.referencesen | [11] Oteiza P. P., Rodriguez D. A., Brignole N. B. Parallel cooperative optimization through hyper-heuristics. Computer Aided Chemical Engineering. 44, 805–810 (2018). | |
dc.relation.referencesen | [12] Burke E. K., Hyde M., Kendall G., Ochoa G., Ozcan E., Woodward J. Exploring hyper-heuristic methodologies with genetic programming. Computational Intelligence. 177–201 (2009). | |
dc.relation.referencesen | [13] Burke E. K., Hyde M. R., Kendall G., Woodward J. Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation. 1559–1565 (2007). | |
dc.relation.referencesen | [14] Burke E. K., Hyde M. R., Kendall G., Woodward J. R. The scalability of evolved on line bin packing heuristics. 2007 IEEE Congress on Evolutionary Computation. 2530–2537 (2007). | |
dc.relation.referencesen | [15] Burke E. K., Hyde M. R., Kendall G. Evolving bin packing heuristics with genetic programming. Parallel Problem Solving from Nature – PPSN IX. 860–869 (2006). | |
dc.relation.referencesen | [16] Dimopoulos C., Zalzala A. M. S. Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software. 32 (6), 489–498 (2001). | |
dc.relation.referencesen | [17] Fukunaga A. Automated discovery of composite SAT variable selection heuristics. Proceedings of the National Conference on Artificial Intelligence (AAAI). 641–648 (2002). | |
dc.relation.referencesen | [18] Fukunaga A. S. Automated discovery of local search heuristics for satisfiability testing. Evol. Comput. 16 (1), 31–61 (2008). | |
dc.relation.referencesen | [19] Fukunaga A. S. Evolving local search heuristics for SAT using genetic programming. Genetic and Evolutionary Computation – GECCO-2004. 483–494 (2004). | |
dc.relation.referencesen | [20] Geiger C. D., Uzsoy R., Aytug H. Rapid modeling and dis- covery of priority dispatching rules: an autonomous learning approach. Journal of Scheduling. 9, 7–34 (2006). | |
dc.relation.referencesen | [21] Keller R. E., Poli R. Cost-benefit investigation of a genetic-programming hyperheuristic. EA 2007: Artificial Evolution. 13–24 (2007). | |
dc.relation.referencesen | [22] Keller R. E., Poli R. Linear genetic programming of parsimonious metaheuristics. 2007 IEEE Congress on Evolutionary Computation. 4508–4515 (2007). | |
dc.relation.referencesen | [23] Acevedo N., Barra C. R., Bolton C. C., Parada V. Automatic design of specialized algorithms for the binary knapsack problem. Expert Systems with Applications. 141, 112908 (2020). | |
dc.relation.referencesen | [24] Eglese R. W. Simulated annealing: A tool for operational research. European Journal of Operational Research. 46 (3), 271–281 (1990). | |
dc.relation.referencesen | [25] Fleischer M. A. Simulated annealing: Past, present, and future. Proceedings of the 1995 Winter Simulation Conference. 155–161 (1995). | |
dc.relation.referencesen | [26] Koulamas C., Antony S. R., Jaen R. A survey of simulated annealing applications to operations research problems. Omega. 22 (1), 41–56 (1994). | |
dc.relation.referencesen | [27] Kirkpatrick S., Gelatt Jr, C. D., Vecchi M. P. Optimization by Simulated Annealing. Science. 220 (4598), 671–680 (1983). | |
dc.relation.referencesen | [28] Romeo F., Sangiovanni-Vincentelli A. A theoretical frame-work for simulated annealing. Algorithmica. 6, Article number: 302 (1991). | |
dc.relation.referencesen | [29] Suman B., Kumar P. A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society. 57 (10), 1143–1160 (2006). | |
dc.relation.referencesen | [30] Alahyane M., Hakim A., Laghrib A., Raghay S. A lattice Boltzmann method applied to the fluid image registration. Applied Mathematics and Computation. 349, 421–438 (2019). | |
dc.rights.holder | © Національний університет “Львівська політехніка”, 2021 | |
dc.subject | гіпер-евристика | |
dc.subject | комбінаторна оптимізація | |
dc.subject | функція вибору | |
dc.subject | модифікована функція вибору | |
dc.subject | евристичний вибір | |
dc.subject | евристична генерація | |
dc.subject | функція Больцмана | |
dc.subject | hyper-heuristics | |
dc.subject | combinatorial optimization | |
dc.subject | choice function | |
dc.subject | modified choice function | |
dc.subject | heuristic selection | |
dc.subject | heuristic generation | |
dc.subject | Boltzmann function | |
dc.title | A modified choice function hyper-heuristic with Boltzmann function | |
dc.title.alternative | Гіпер-евристика модифікованої функції вибору за функцією Больцмана | |
dc.type | Article |
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