Nature-inspired adaptive differential evolution: A unified meta-heuristic framework for complex engineering optimisation and UAV path planning

• Proposes Nature-Inspired Adaptive Differential Evolution (NIADE), a novel meta-heuristic framework grounded in holistic natural principles. • Demonstrates NIADE's superiority via CEC2017, CEC2022 benchmark functions, and CEC2020 Real-World constrained problems. • Successfully applies NIADE to UAV path planning, providing an innovative solution approach. • Explores future improvements and its potential for diverse optimization tasks. Unlike traditional meta-heuristic algorithms that typically draw inspiration from a single biological or collective behaviour, this paper introduces a novel meta-heuristic approach from a holistic natural perspective. Drawing on the principles of biological evolution, collective behaviours within populations, and the self-regulation mechanisms of ecosystems, the proposed algorithm is termed Nature-Inspired Adaptive Differential Evolution (NIADE). By integrating multiple strategies and global optimisation concepts, NIADE effectively addresses complex problems characterised by numerous interacting variables, thus overcoming the limitations inherent in existing algorithms that depend primarily on single strategies or local optimisation methods. This integration provides innovative pathways for solving complex optimisation challenges. The algorithm's performance is evaluated using benchmark functions from CEC2017 and CEC2022 and compared with seven prominent algorithms. Statistical analysis via Wilcoxon rank-sum tests and Friedman test statistics confirms the superiority of NIADE. Furthermore, the effectiveness of NIADE in solving multi-constrained real-world engineering problems is validated through the CEC2020 Real-World Constrained Problems set. Additionally, its applicability to unmanned aerial vehicle (UAV) path planning is demonstrated through modelling and practical experiments, presenting a promising new solution in this domain. Finally, the paper discusses potential improvements and future research directions for the NIADE algorithm. The algorithm's source code is available in the Appendix D.