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Introduction
Published in Joseph Y.-T. Leung, Handbook of SCHEDULING, 2004
The main idea behind the Great Deluge algorithm is to direct the search of the solution space along a trajectory by changing the acceptance level of the cost function values. This has been implemented in a multicriteria environment (Petrovic and Bykov, 2003). The method requires the timetable officer to specify a reference solution that reflects his/her preference toward the criteria values. It can be obtained either by some other method (not necessarily a multicriteria one) or the timetable officer sets the values of the criteria that he/she aims at in the solution. The method conducts the search along the trajectory that is drawn in the criteria space between the reference solution and the origin, aiming to find a solution that is better than the reference one. The direction of the search is toward the origin because it represents the ideal solution. As mentioned, the ideal solution is usually not achievable in real-world timetabling problems. However, the search does not start from the reference point itself, because it may either lay in the local optimum (thus the search will be stuck), or if the point was set manually it may not be possible to find a solution that corresponds to this point in the criteria space. The search starts from a solution that is in the vicinity of the reference point. Therefore, the search is divided into two phases. The aim of the first phase is to find a solution which is close to the reference one. The search starts from a randomly chosen initial solution toward the reference point and stops when there is no improvement in the cost function in a predefined number of iterations. In the second phase, the search is conducted along the trajectory drawn between the reference point and the origin.
A Recurrence Population-based Great Deluge Algorithm with Independent Quality Estimation for Feature Selection from Academician Data
Published in Applied Artificial Intelligence, 2021
Najmeh Sadat Jaddi, Salwani Abdullah, Mohd Zakree Ahmad Nazri
Great deluge algorithm (GD) (Dueck 1993) (Figure 1), which is a single-solution, in many features is similar to hill climbing (HC) and simulated annealing (SA) algorithms. The name of GD comes from the similarity that in a great deluge someone does hill climbing and attempt to track any direction. In this climbing the person tries to not get his/her feet wet expecting to find a way up while the water level is increased. In original GD a single solution represents the position of person and the new solution is calculated to find a new position of the person. The value of level represents the water level and is increased in each iteration based on the quality of current solutions and also estimated quality of the final solution. The acceptance of the current solution depends on value of level in the current iteration. GD has been employed to solve many problems in literature such as researches in (Acan and Ahmet 2020; Baykasoglu 2012; Baykasoglu, Durmusoglu, and Kaplanoglu 2011; McCollum et al. 2009; Mcmullan 2007; Mosbah and Dao 2010; Nahas et al. 2008; Nourelfath, Nahas, and Montreuil 2007).