TY - CHAP
T1 - Dynamic problems and nature inspired meta-heuristics
AU - Hendtlass, Tim
AU - Moser, Irene
AU - Randall, Marcus
PY - 2009
Y1 - 2009
N2 - Biological systems have often been used as the inspiration for search techniques to solve continuous and discrete combinatorial optimisation problems. One of the key aspects of biological systems is their ability to adapt to changing environmental conditions. Yet, biologically inspired optimisation techniques are mostly used to solve static problems (problems that do not change while they are being solved) rather than their dynamic counterparts. This is mainly due to the fact that the incorporation of temporal search control is a challenging task. Recently, however, a greater body of work has been completed on enhanced versions of these biologically inspired meta-heuristics, particularly genetic algorithms, ant colony optimisation, particle swarm optimisation and extremal optimisation, so as to allow them to solve dynamic optimisation problems. This survey chapter examines representative works and methodologies of these techniques on this important class of problems.
AB - Biological systems have often been used as the inspiration for search techniques to solve continuous and discrete combinatorial optimisation problems. One of the key aspects of biological systems is their ability to adapt to changing environmental conditions. Yet, biologically inspired optimisation techniques are mostly used to solve static problems (problems that do not change while they are being solved) rather than their dynamic counterparts. This is mainly due to the fact that the incorporation of temporal search control is a challenging task. Recently, however, a greater body of work has been completed on enhanced versions of these biologically inspired meta-heuristics, particularly genetic algorithms, ant colony optimisation, particle swarm optimisation and extremal optimisation, so as to allow them to solve dynamic optimisation problems. This survey chapter examines representative works and methodologies of these techniques on this important class of problems.
UR - http://www.scopus.com/inward/record.url?scp=78049260556&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-01262-4_4
DO - 10.1007/978-3-642-01262-4_4
M3 - Chapter
AN - SCOPUS:78049260556
SN - 9783642012617
VL - 210
T3 - Studies in Computational Intelligence
SP - 79
EP - 109
BT - Biologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applications
ER -