Solving the high school timetabling problem using a hybrid cat swarm optimization based algorithm

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

“…Silhouette coefficient was used to verify these results in which was between 0.7-0.9 [28] Used CSO to optimize the network structures for pinning control CSO outperformed a number of heuristic methods [86] Applied CSO with local search refining procedure to address high school timetabling problem CSO outperformed the Genetic Algorithm (GA), Evolutionary Algorithm (EA), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Artificial Fish Swarm (AFS). [27] BCSO with Dynamic mixture ratios to address the Manufacturing Cell Design Problem BCSO can effectively tackle the MCDP problem regardless of the scale of the problem [87] used CSO to find the optimal Reservoir Operation in water resource management CSO outperformed GA [88] Applied CSO to classify the the feasibility of small loans in banking systems CSO resulted in 76% of accuracy in comparison to 64% resulted from OLR procedure.…”

Models and algorithms for high school timetabling problems

“…In this case, such bounds can be compared with solutions found by heuristic algorithms to provide information on optimality gaps. Some extended formulations proposed to HSTP have shown strong lower bounds (PAPOUTSIS; VALOUXIS; DORNELES;BURIOL, 2017), and recent heuristic approaches (FON-SECA; SANTOS; SAVINIEC;CONSTANTINO, 2017;SKOULLIS;BELIGIANNIS, 2017) have found good quality solutions. However, optimality gaps for medium and large size instances are still not sufficiently small to conclude that state-of-the-art formulations and heuristics are effective for many practical instances.…”

“…The primary goal of such studies is to develop strategies to generate good quality solutions within reasonable computational effort. Recently proposed metaheuristics for the HSTP can be found in , Dorneles, Araújo and Buriol (2014), , , Saviniec and Constantino (2017) and Skoullis, Tassopoulos and Beligiannis (2017). These methods are shown to be efficient in the sense that near-optimal solutions are consistently found for different input instances.…”

“…Beligiannis et al (2008) introduced a set of real-case instances from Greek high schools 3 that also became the subject of several studies LIKOTHANASSIS, 2009;ZHANG et al, 2010;BELIGIANNIS, 2012a;SKOULLIS;BELIGIANNIS, 2017). The state-of-the-art heuristic method reported for the Greek problem is a cat swarm optimization based algorithm (SKOULLIS; TASSOPOULOS; BELIGIANNIS, 2017).…”

“…Given these limitations, many researchers have focused on combining heuristic algorithms and relaxation methods to obtain high-performance hybrid approaches and optimality bounds. On the one hand, research on heuristic algorithms (e.g., BELIGIANNIS, 2012a;DORNELES;SAVINIEC;CONSTANTINO, 2017;SKOULLIS;BELIGIANNIS, 2017)) aims at the computation of high-quality solutions with reduced computational effort. On the other hand, the research on relaxation methods (e.g., decomposition by column generation (PAPOUTSIS; VALOUXIS; DORNELES;BURIOL, 2017)) aims at the computation of tight lower bounds on the optimal solutions.…”