Abstract
The importance of optimization and NP-problem solving cannot be overemphasized. The usefulness
and popularity of evolutionary computing methods are also well established. There are
various types of evolutionary methods; they are mostly sequential but some of them have parallel
implementations as well.We propose a multi-population method to parallelize the Imperialist
CompetitiveAlgorithm. The algorithm has been implementedwith the Message Passing Interface
on 2 computer platforms, and we have tested our method based on shared memory and message
passing architectural models. An outstanding performance is obtained, demonstrating that
the proposed method is very efficient concerning both speed and accuracy. In addition, compared
with a set of existingwell-known parallel algorithms, our approach obtainsmore accurate results
within a shorter time period.
| Original language | Undefined/Unknown |
|---|---|
| Pages (from-to) | – |
| Journal | Concurrency and Computation: Practice and Experience |
| Volume | 30 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2018 |
| MoE publication type | A1 Journal article-refereed |