Hybrid particle swarm optimization with particle elimination for the high school timetabling problem

Abstract

In this paper, a PSO-based algorithm that hybridized Particle Swarm Optimization (PSO) and Hill Climbing (HC) is applied to high school timetabling problem. This hybrid has two features, a novel solution transformation and particle elimination. The proposed methodologies are tested on the XHSTT-2014 dataset (which is relatively new for the school timetabling problem) plus other additional instances. The experimental results show that the proposed algorithm is efective in solving small and medium instances compared to standalone HC and better than the conventional PSO for most instances. In a comparison to the state of the art methods, it achieved the lowest mean of soft constraint violations for 7 instances and the lowest mean of hard constraint violations for 1 instance.

An Effective Local Particle Swarm Optimization-Based Algorithm for Solving the School Timetabling Problem

“…Meanwhile, Katsaragakis et al [28] presented an algorithm based on artificial fish swarm optimization (AFSO), showing that AFSO cannot outperform PSO in this problem. Similarly, Tan et al [29] hybridized PSO and hill climbing (HC).…”

Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

“…Multiple Solutions are built to explore search space and move toward an optimal solution. Ant Colony algorithm 18 , Particle Swarm Optimization (PSO) 9 , and Genetic algorithm (GA) 19 are some population-based approaches. Several metaheuristic methods have been used for UCTP [13][14][15][16]18 .…”

“…Many researchers believe that getting a nearoptimal solution faster is better than achieving an optimal solution in timetabling problems. Recently, PSO has been used for school timetabling problems 1468 9 and produced reasonable results. Some remarkable works on UCTP have been done by [13][14][15][16]18 using different heuristic, metaheuristic 31 , and hyperheuristic methods.…”

“…PSO has widely been used in the literature [6][7][8][9] . This research proposed a PSO-based selection hyper-heuristic for solving UCTP where PSO will work as a higher-level methodology that will use low-level heuristics to obtain the optimal solution.…”

An Improved Discrete Grey Wolf Optimizer for the Nurse Rostering Problem

“…Combinatorial optimization problems (COP), which include vehicle routing [1,2], traveling salesman [3], high school timetabling [4], personnel scheduling [5,6] and job shop scheduling [7,8] have had many approaches (both exact and approximate) proposed to address them. The Nurse Rostering Problem (NRP) or Nurse Scheduling Problem (NSP) was introduced by Warner [9] and Miller et.…”