Name
Title | ||
|---|---|---|
The great salmon run: a novel bio-inspired algorithm for artificial system design and optimisation |
Abstract | ||
|---|---|---|
The major application of stochastic intelligent methods in optimisation, control and management of complex systems is transparent. Many researchers try to develop collective intelligent techniques and hybrid meta-heuristic models for improving the reliability of such optimisation algorithms. In this paper, a new optimisation method that is the simulation of 'the great salmon run' (TGSR) is developed. This simulation provides a powerful tool for optimising complex multi-dimensional and multi-modal problems. For demonstrating the high robustness and acceptable quality of TGSR, it is compared with most of the well-known proposed optimisation techniques such as parallel migrating genetic algorithm (PMGA), simulate annealing (SA), differential evolutionary algorithm (DEA), particle swarm optimisation (PSO), bee algorithm (BA), artificial bee colony (ABC), firefly algorithm (FA) and cuckoo search (CS). The obtained results confirm the predominance of the proposed method in both robustness and quality in different optimisation problems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1504/IJBIC.2012.049889 | IJBIC |
Keywords | Field | DocType |
differential evolutionary algorithm,acceptable quality,bee algorithm,parallel migrating genetic algorithm,new optimisation method,firefly algorithm,particle swarm optimisation,optimisation algorithm,well-known proposed optimisation technique,great salmon run,artificial system design,novel bio-inspired algorithm,different optimisation problem,meta heuristics | Evolutionary algorithm,Systems design,Robustness (computer science),Artificial intelligence,Genetic algorithm,Metaheuristic,Particle swarm optimization,Mathematical optimization,Algorithm,Cuckoo search,Firefly algorithm,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 5 | 1758-0366 |
Citations | PageRank | References |
16 | 0.72 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmad Mozaffari | 1 | 280 | 24.01 |
| Alireza Fathi | 2 | 930 | 40.79 |
| Saeed Behzadipour | 3 | 69 | 8.16 |