A general framework of multi-population methods with clustering in undetectable dynamic environments

Copyright @ 2011 IEEETo solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different sub-areas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multi-population methods are applied, e.g., how to create multiple populations, how to maintain them in different sub-areas, and how to deal with the situation where changes can not be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multi-population methods on the moving peaks benchmark

Implementasi Algoritma Integer Linear Programming Untuk Sistem Informasi Penjadwalan Ruangan Di Fakultas Ilmu Komputer Universitas Indonesia

Permasalahan konflik penjadwalan ruangan (timetabling) sering dihadapi hampir sebagian besar institusi akademis di Indonesia, salah satunya di Fakultas Ilmu Komputer Universitas Indonesia (Fasilkom UI). Peningkatan jumlah mahasiswa setiap tahun yang tidak diikuti oleh peningkatan jumlah dan kapasitas kelas menjadi faktor penyebab utama. Selama ini sistem penjadwalan masih dilakukan secara manual, sehingga membutuhkan waktu yang relatif lama dan menyebabkan optimasi pengalokasian kebutuhan ruangan menjadi kurang efisien. Penelitian ini bertujuan untuk menemukan pendekatan yang sesuai dalam menyelesaikan masalah timetabling tersebut. Beberapa pendekatan yang dapat digunakan untuk menyelesaikan masalah ini antara lain algoritma Tabu Search, Simmulated Annealing, Graph Coloring, dan Integer Linear Programming (ILP). Dalam penelitian ini, peneliti menggunakan algoritma ILP karena ILP merupakan model yang paling tepat untuk menyelesaikan masalah timetabling di Fasilkom UI. Algoritma ini dapat meminimalkan waktu yang diperlukan untuk melakukan penjadwalan dari sebulan menjadi hitungan menit. Room scheduling conflict issues (timetabling) are facing most of the academic institutions in Indonesia, one is in the Faculty of Computer Science (Fasilkom) Universitas Indonesia (UI). In the number of students each year followed by no increase in the number and capacity of the class became the main factor. During this scheduling system is still done manually so it takes a relatively long time so that the optimization is less efficient allocation of space requirements. This study aims to find an appropriate approach in solving the timetabling problem. Several approaches can be used to solve these problems include Tabu Search algorithm, Simmulated Annealing, Graph Coloring, and Integer Linear Programming (ILP). In this study we used the ILP algorithm for ILP is the most appropriate model to solve the timetabling problem in Fasilkom UI. This algorithm can minimize the time required to perform the scheduling of a month becomes a matter of minutes

IMPLEMENTASI ALGORITMA INTEGER LINEAR PROGRAMMING UNTUK SISTEM INFORMASI PENJADWALAN RUANGAN DI FAKULTAS ILMU KOMPUTER UNIVERSITAS INDONESIA

Permasalahan konflik penjadwalan ruangan (timetabling) sering dihadapi hampir sebagian besar institusi akademis di Indonesia, salah satunya di Fakultas Ilmu Komputer Universitas Indonesia (Fasilkom UI). Peningkatan jumlah mahasiswa setiap tahun yang tidak diikuti oleh peningkatan jumlah dan kapasitas kelas menjadi faktor penyebab utama. Selama ini sistem penjadwalan masih dilakukan secara manual, sehingga membutuhkan waktu yang relatif lama dan menyebabkan optimasi pengalokasian kebutuhan ruangan menjadi kurang efisien. Penelitian ini bertujuan untuk menemukan pendekatan yang sesuai dalam menyelesaikan masalah timetabling tersebut. Beberapa pendekatan yang dapat digunakan untuk menyelesaikan masalah ini antara lain algoritma Tabu Search, Simmulated Annealing, Graph Coloring, dan Integer Linear Programming (ILP). Dalam penelitian ini, peneliti menggunakan algoritma ILP karena ILP merupakan model yang paling tepat untuk menyelesaikan masalah timetabling di Fasilkom UI. Algoritma ini dapat meminimalkan waktu yang diperlukan untuk melakukan penjadwalan dari sebulan menjadi hitungan menit. Room scheduling conflict issues (timetabling) are facing most of the academic institutions in Indonesia, one is in the Faculty of Computer Science (Fasilkom) Universitas Indonesia (UI). In the number of students each year followed by no increase in the number and capacity of the class became the main factor. During this scheduling system is still done manually so it takes a relatively long time so that the optimization is less efficient allocation of space requirements. This study aims to find an appropriate approach in solving the timetabling problem. Several approaches can be used to solve these problems include Tabu Search algorithm, Simmulated Annealing, Graph Coloring, and Integer Linear Programming (ILP). In this study we used the ILP algorithm for ILP is the most appropriate model to solve the timetabling problem in Fasilkom UI. This algorithm can minimize the time required to perform the scheduling of a month becomes a matter of minutes

How to exploit fitness landscape properties of timetabling problem: A newoperator for quantum evolutionary algorithm

© 2020 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.eswa.2020.114211The fitness landscape of the timetabling problems is analyzed in this paper to provide some insight into theproperties of the problem. The analyses suggest that the good solutions are clustered in the search space andthere is a correlation between the fitness of a local optimum and its distance to the best solution. Inspiredby these findings, a new operator for Quantum Evolutionary Algorithms is proposed which, during the searchprocess, collects information about the fitness landscape and tried to capture the backbone structure of thelandscape. The knowledge it has collected is used to guide the search process towards a better region in thesearch space. The proposed algorithm consists of two phases. The first phase uses a tabu mechanism to collectinformation about the fitness landscape. In the second phase, the collected data are processed to guide thealgorithm towards better regions in the search space. The algorithm clusters the good solutions it has foundin its previous search process. Then when the population is converged and trapped in a local optimum, itis divided into sub-populations and each sub-population is designated to a cluster. The information in thedatabase is then used to reinitialize the q-individuals, so they represent better regions in the search space.This way the population maintains diversity and by capturing the fitness landscape structure, the algorithmis guided towards better regions in the search space. The algorithm is compared with some state-of-the-artalgorithms from PATAT competition conferences and experimental results are presented.Peer reviewe

Examination timetabling at the University of Cape Town: a tabu search approach to automation

With the rise of schedules and scheduling problems, solutions proposed in literature have expanded yet the disconnect between research and reality remains. The University of Cape Town's (UCT) Examinations Office currently produces their schedules manually with software relegated to error-checking status. While they have requested automation, this study is the first attempt to integrate optimisation techniques into the examination timetabling process. Tabu search and Nelder-Mead methodologies were tested on the UCT November 2014 examination timetabling data with tabu search proving to be more effective, capable of producing feasible solutions from randomised initial solutions. To make this research more accessible, a user-friendly app was developed which showcased the optimisation techniques in a more digestible format. The app includes data cleaning specific to UCT's data management system and was presented to the UCT Examinations Office where they expressed support for further development: in its current form, the app would be used as a secondary tool after an initial solution has been manually obtained

New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201