A natural-inspired optimization machine based on the annual migration of salmons in nature

TL;DR

This paper introduces a new bio-inspired optimization algorithm based on salmon migration, demonstrating competitive performance in complex, multi-modal problems compared to established methods.

Contribution

The paper develops a novel metaheuristic algorithm inspired by salmon migration, showing its effectiveness and robustness in solving complex optimization problems.

Findings

The TGSR algorithm performs well on benchmark problems.

It outperforms several well-known optimization techniques.

It demonstrates robustness and high solution quality.

Abstract

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.