TY  - JOUR
T1  - A Comparative Study on three Component Selection Mechanisms for
Hyper-Heuristics in Expensive Optimization
AU - Hui Ong, Jia AU - Teo, Jason 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 12
SP  - 4535
EP  - 4541
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.4535.4541
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.4535.4541
KW  - Hyper-heuristics
KW  -expensive optimization
KW  -hyper-heuristic component selection
KW  -MVMO
KW  -minor
KW  -TSAMA
AB  - Numerous studies in optimization problems often lead to tailoring a specific algorithm to adapt to the
problem instances, especially in expensive optimization problems. The focus of these researches is often to
challenge and outperform another algorithm in the specific problem instant. Once the problem instants changes,
more tailoring of the algorithm has to be done in order for the algorithm to perform at an optimum level.
Expensive optimization often requires a large amount of resources to run on such as computational power, high
run-time budget and consumes a lot of time. As such, tailoring an algorithm to perform well in expensive
optimization requires a lot of expertise and time. Hyper-heuristics is an approach that utilizes a set of Low-Level
Heuristic (LLH) and a selection mechanism to solve expensive optimization problems. The main aim of using
hyper-heuristics is to be able to apply a general yet efficient optimizationalgorithm to all expensive problem
instances with very minor or minimal tweaks. In this study, three different selection mechanisms for
Hyper-heuristics are introduced and compared against one of the top performing expensive optimization
algorithms known asthe Mean-Variance Mapping Optimization (MVMO) as described in the CEC 2015 and 2016
expensive optimization competitions. Three variants of hyper-heuristics were used in this study, Simple Random
All Moves Acceptance (SRAMA), Tabu-Search All Moves Acceptance (TSAMA) and Random Gradient
Descent All Moves Acceptance (RGDAMA). The set of LLH will also include a simplified version of MVMO.
The performance of hyper-heuristics is highly encouraging against a specifically tailored algorithm for CEC test
set of expensive optimization problems.
ER  -