A natural-inspired optimization machine based on the annual migration of salmons in nature
This is an incremental contribution to the field of optimization algorithms, offering a bio-inspired method for engineers and researchers dealing with complex optimization problems.
The authors tackled the problem of optimizing complex non-convex, multidimensional, and multimodal functions by developing a new metaheuristic algorithm called The Great Salmon Run (TGSR), which outperformed established methods like Simulated Annealing and Particle Swarm Optimization in robustness and quality.
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.