An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges
As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
[HTML][HTML] Recent advances in selection hyper-heuristics
Hyper-heuristics have emerged as a way to raise the level of generality of search techniques
for computational search problems. This is in contrast to many approaches, which represent …
for computational search problems. This is in contrast to many approaches, which represent …
Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations
In recent algorithmic family simulates different biological processes observed in Nature in
order to efficiently address complex optimization problems. In the last years the number of …
order to efficiently address complex optimization problems. In the last years the number of …
Hyper-heuristics: A survey of the state of the art
Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the
goal of automating the design of heuristic methods to solve hard computational search …
goal of automating the design of heuristic methods to solve hard computational search …
Hyflex: A benchmark framework for cross-domain heuristic search
This paper presents HyFlex, a software framework for the development of cross-domain
search methodologies. The framework features a common software interface for dealing with …
search methodologies. The framework features a common software interface for dealing with …
A sequence-based selection hyper-heuristic utilising a hidden Markov model
A Kheiri, E Keedwell - Proceedings of the 2015 annual conference on …, 2015 - dl.acm.org
Selection hyper-heuristics are optimisation methods that operate at the level above
traditional (meta-) heuristics. Their task is to evaluate low level heuristics and determine …
traditional (meta-) heuristics. Their task is to evaluate low level heuristics and determine …
A tensor based hyper-heuristic for nurse rostering
Nurse rostering is a well-known highly constrained scheduling problem requiring
assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to …
assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to …
Assessing hyper-heuristic performance
Limited attention has been paid to assessing the generality performance of hyper-heuristics.
The performance of hyper-heuristics has been predominately assessed in terms of optimality …
The performance of hyper-heuristics has been predominately assessed in terms of optimality …
Fair-share ILS: a simple state-of-the-art iterated local search hyperheuristic
In this work we present a simple state-of-the-art selection hyperheuristic called Fair-Share
Iterated Local Search (FS-ILS). FS-ILS is an iterated local search method using a …
Iterated Local Search (FS-ILS). FS-ILS is an iterated local search method using a …
Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework
HyFlex is a recently proposed software framework for implementing hyper-heuristics and
domain-independent heuristic optimisation algorithms [13]. Although it was originally …
domain-independent heuristic optimisation algorithms [13]. Although it was originally …