
Biological systems are, by their very nature, adaptive. However, the meta-heuristic search algorithms inspired by them have mainly been applied to static problems (i.e., problems that do not change while they are being solved). Recently, a greater body of work has been completed on the newer meta-heuristics, particularly ant colony optimisation, particle swarm optimisation and extremal optimisation. This survey paper examines representative works and methodologies of these techniques on this class of problems. Beyond this we outline the limitations of these methods.
inspired, dynamic, problems, optimisation, Theory and Algorithms, extremal optimisation, biological systems, nature, Numerical Analysis and Computation, genetic algorithms, evoluationary and adaptive dynamics, meta-heuristics, ant colony optimisation, particle swarm optimisation
inspired, dynamic, problems, optimisation, Theory and Algorithms, extremal optimisation, biological systems, nature, Numerical Analysis and Computation, genetic algorithms, evoluationary and adaptive dynamics, meta-heuristics, ant colony optimisation, particle swarm optimisation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 5 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
