  Bio inspiration is a branch of artificial simulation science that shows
pervasive contributions to variety of engineering fields such as automate
pattern recognition, systematic fault detection and applied optimization. In
this paper, a new metaheuristic optimizing algorithm that is the simulation of
The Great Salmon Run(TGSR) is developed. The obtained results imply on the
acceptable performance of implemented method in optimization of complex non
convex, multi dimensional and multi-modal problems. To prove the superiority of
TGSR in both robustness and quality, it is also compared with most of the well
known proposed optimizing techniques such as Simulated Annealing (SA), Parallel
Migrating Genetic Algorithm (PMGA), Differential Evolutionary Algorithm (DEA),
Particle Swarm Optimization (PSO), Bee Algorithm (BA), Artificial Bee Colony
(ABC), Firefly Algorithm (FA) and Cuckoo Search (CS). The obtained results
confirm the acceptable performance of the proposed method in both robustness
and quality for different bench-mark optimizing problems and also prove the
authors claim.
