A comparative study of heuristic algorithms to solve maintenance scheduling problem (original) (raw)

Meta-heuristics : theory & applications

1996

Meta-Heuristics: An Overview I.H. Osman, J.P. Kelly. Genetic Algorithms: A Parallel Genetic Algorithm for the Set Partitioning Problem D. Levine. Evolutionary Computation and Heuristics Z. Michalewicz. Gene Pool Recombination in Genetic Algorithms H. Muhlenbein, H.-M. Voigt. Genetic and Local Search Algorithms as Robust and Simple Optimization Tools M. Yagiura, T. Ibaraki. Networks and Graphs: Comparison of Heuristic Algorithms for the Degree Constrained Minimum Spanning Tree G. Craig, et al. An Aggressive Search Procedure for the Bipartite Drawing Problem R. Marti. Guided Search for the Shortest Path on Transportation Networks Y.M. Sharaiha, R. Thaiss. Scheduling and Control: A Metaheuristic for the Timetabling Problem H. Abada, E. El-Darzi. Complex Sequencing Problems and Local Search Heuristics P. Brucker, H. Hurink. Heuristic Algorithms for Single Processor Scheduling with Earliness and Flow Time Penalties M. Dell'Amico, et al. Heuristics for the Optimal Control of Thermal E...

A Comprehensive Review on Meta-Heuristic Algorithms and their Classification with Novel Approach

2021

Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems has increased dramatically and it is widely used in various fields. These algorithms, in contrast to exact optimization methods, find the solutions which are very close to the global optimum solution as possible, in such a way that this solution satisfies the threshold constraint with an acceptable level. Most of the meta-heuristic algorithms are inspired by natural phenomena. In this research, a comprehensive review on meta-heuristic algorithms is presented to introduce a large number of them (i.e. about 110 algorithms). Moreover, this research provides a brief explanation along with the source of their inspiration for each algorithm. Also, these algorithms are categorized based on the type of algorithms (e.g. swarm-based, evolutionary, physics-ba...