Name
Title | ||
|---|---|---|
A dynamic archive niching differential evolution algorithm for multimodal optimization |
Abstract | ||
|---|---|---|
Highly multimodal landscapes with multiple local/global optima represent common characteristics in real-world applications. Many niching algorithms have been proposed in the literature which aim to search such landscapes in an attempt to locate as many global optima as possible. However, to locate and maintain a large number of global solutions, these algorithms are substantially influenced by their parameter values, such as a large population size. Here, we propose a new niching Differential Evolution algorithm that attempts to overcome the population size influence and produce good performance almost independently of its population size. To this end, we incorporate two mechanisms into the algorithm: a control parameter adaptation technique and an external dynamic archive along with a reinitialization mechanism. The first mechanism is designed to efficiently adapt the control parameters of the algorithm, whilst the second one is responsible for enabling the algorithm to investigate unexplored regions of the search space and simultaneously keep the best solutions found by the algorithm. The proposed approach is compared with two Differential Evolution variants on a recently proposed benchmark suite. Empirical results indicate that the proposed niching algorithm is competitive and very promising. It exhibits a robust and stable behavior, whilst the incorporation of the dynamic archive seems to tackle the population size influence effectively. Moreover, it alleviates the problem of having to fine-tune the population size parameter in a niching algorithm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CEC.2013.6557556 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
evolutionary computation,optimisation,search problems,control parameter adaptation technique,dynamic archive niching differential evolution algorithm,multimodal landscapes,multimodal optimization,multiple local-global optima,population size parameter,reinitialization mechanism,search space | Mathematical optimization,Suite,Computer science,Evolutionary computation,Differential evolution,Population size,Artificial intelligence,Differential evolution algorithm,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4799-0452-5 | 24 | 0.69 |
References | Authors | |
24 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael G. Epitropakis | 1 | 81 | 3.67 |
| Xiaodong Li | 2 | 746 | 49.17 |
| Edmund K. Burke | 3 | 114 | 4.19 |