A Bio-Inspired Optimization: A New Meta-heuristic Based on Animal Adaptability

Organisms continuously adapt to changing resources and environmental conditions to ensure survival. Such adaptations may involve shifts in diet, habitat, or hunting strategies. Species that fail to adapt often face extinction, as evidenced by many species that no longer exist. Adaptation can generally be understood as a two-stage process. The first stage encompasses phenotypic adjustments, which occur in response to factors such as climate change, food availability, or the development of adaptive behaviors. These short-term changes increase the chances of survival for individuals by favoring the fittest under immediate conditions. The second stage involves genetic changes, where offspring chromosomes are formed through crossover and mutation of parental chromosomes. These genetic variations may either enhance the survival of the next generation or reduce it, thereby influencing long-term evolutionary success. In this study, a novel metaheuristic algorithm is introduced, inspired by the adaptability of socially living animals. The proposed algorithm was evaluated on a set of standard benchmark functions and compared with several well-established optimization methods. The results indicate that the proposed algorithm outperforms others in terms of convergence speed, solution accuracy, and robustness. Finally, to demonstrate its practical applicability, the algorithm was applied to an engineering problem involving air-conditioner load scheduling, where it achieved superior performance.

کلیدواژه ها:

Animals’ adaptability algorithm ، meta-heuristic algorithm ، Optimization ، decision-making