梳理生物启发算法的现状与短板，指明改进方向。
A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs
- 按演化、群体、物理等八类归纳算法原理与适用场景。
- 指出多数算法创新性不足，缺乏严格验证。
- 适合关注算法可靠性与未来研究方向的研究者。
生物启发算法利用进化、群体行为、觅食和植物生长等自然过程解决复杂、非线性、高维优化问题。然而，大量此类算法在应用于相关领域前需更严格的评估。本文将算法分为八类：演化、群体智能、物理启发、生态系统与植物基、捕食-被捕食、神经启发、人类启发及混合方法，综述其原理、优势、新颖性及关键局限。针对诸多算法的创新性问题提出批判，并展示主流算法在机器学习、工程设计、生物信息学和智能系统中的适用场景。强调混合策略、参数调优与自适应机制的最新进展。最后，识别可扩展性、收敛性、可靠性和可解释性等开放挑战，为未来研究提供方向。本工作旨在为研究人员和实践者提供理解生物启发算法当前格局与未来发展路径的参考。
原文摘要 · Abstract (English)
Bio-inspired algorithms utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. However, a plethora of these algorithms require a more rigorous review before making them applicable to the relevant fields. This survey categorizes these algorithms into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their principles, strengths, novelty, and critical limitations. We provide a critique on the novelty issues of many of these algorithms. We illustrate some of the suitable usage of the prominent algorithms in machine learning, engineering design, bioinformatics, and intelligent systems, and highlight recent advances in hybridization, parameter tuning, and adaptive strategies. Finally, we identify open challenges such as scalability, convergence, reliability, and interpretability to suggest directions for future research. This work aims to serve as a resource for both researchers and practitioners interested in understanding the current landscape and future directions of reliable and authentic advancement of bio-inspired algorithms.
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