The Colony Predation Algorithm

TLDR

The study introduces the Colony Predation Algorithm, a stochastic optimizer inspired by animal predation, and proposes a novel cross‑border handling strategy to enhance exploitation. CPA models hunting group tactics—dispersal, encirclement, support of the most promising hunter, and target switching—using a success‑rate‑adjusted mathematical framework that also incorporates a cross‑border optimal‑position replacement, and it was benchmarked against leading metaheuristics and engineering design problems. The algorithm achieved competitive or superior performance across diverse search landscapes and engineering design benchmarks compared to state‑of‑the‑art metaheuristics.

Abstract

Abstract This paper proposes a new stochastic optimizer called the Colony Predation Algorithm (CPA) based on the corporate predation of animals in nature. CPA utilizes a mathematical mapping following the strategies used by animal hunting groups, such as dispersing prey, encircling prey, supporting the most likely successful hunter, and seeking another target. Moreover, the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals’ selective abandonment behavior. This paper also presents a new way to deal with cross-border situations, whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm’s exploitation ability. The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm’s efficacy in optimizing engineering problems. The results show that the proposed algorithm exhibits competitive, superior performance in different search landscapes over the other algorithms. Moreover, the source code of the CPA will be publicly available after publication.