# Bio-Inspired Swarm Confrontation Algorithm for Complex Hilly Terrains

## TL;DR

This paper introduces a new bio-inspired algorithm for swarms in games on hilly terrain, improving coordination and win rates over existing methods.

## Contribution

The paper introduces a decentralized swarm algorithm inspired by animal hunting strategies for complex terrains in games.

## Key findings

- The algorithm achieves a confrontation win rate exceeding 80% in complex hilly terrains.
- It outperforms existing techniques in engagement efficiency and survivability.
- Two novel performance indices are introduced to better assess algorithmic effectiveness.

## Abstract

This paper explores a bio-inspired swarm confrontation algorithm specifically designed for complex hilly terrains in the context of electronic games. The novelty of the proposed algorithm lies in its utilization of biologically inspired strategies to facilitate adaptive and efficient decision-making in dynamic environments. Drawing from the collective hunting behaviors of various animal species, this paper distills two key confrontation strategies: focused fire for target selection and flanking encirclement for movement coordination and attack execution. These strategies are embedded into a decentralized swarm decision-making framework, enabling agents to exhibit enhanced responsiveness and coordination in complex gaming landscapes. To validate its effectiveness, extensive experiments were conducted, comparing the proposed approach against three established algorithms. The results demonstrate that this method achieves a confrontation win rate exceeding 80%, outperforming existing techniques in both engagement efficiency and survivability. Additionally, two novel performance indices, namely the average agent quantity loss rate and the average health loss rate, are introduced to provide a more comprehensive assessment of algorithmic effectiveness. Furthermore, the impact of key algorithmic parameters on performance indices is analyzed, offering insights into the adaptability and robustness of the proposed algorithm.