A sequoia-ecology-based metaheuristic optimisation algorithm for multi-constraint engineering design and UAV path planning

Abstract

• Proposes the Sequoia Optimization Algorithm (SequoiaOA), a novel metaheuristic method inspired by the self-regulating dynamics and resilience of sequoia forest ecosystems, diverging from conventional singular biological or phenomenological inspirations. • Develops a comprehensive ecosystem-driven framework, including mathematical modeling, system analysis, and validation through CEC benchmarks and multi-constrained engineering problems. • Applies SequoiaOA to UAV path planning, demonstrating its practical efficacy in optimizing complex trajectory design for advanced autonomous navigation. • Reveals broad adaptability and future potential, supported by statistically superior performance and diverse applications spanning optimization, control, and adaptive systems. Current metaheuristic algorithms are typically confined to inspiration from singular perspectives, isolated biological behaviours, or individual phenomena, resulting in inherent limitations that have motivated this study. Diverging from conventional approaches, this paper explores complex biological systems from an ecosystem perspective and conducts comprehensive system modelling, analysis, and algorithmic innovation. Specifically, a novel metaheuristic optimization algorithm inspired by the self-regulating and restorative phenomena observed in sequoia forest ecosystems called the Sequoia Optimization Algorithm (SequoiaOA) is proposed. The sequoia ecosystem exhibits distinctive characteristics including collective growth, resource sharing and networking, adaptability and resilience, reproduction and diversity, and elite retention. Building upon these phenomena, this work presents the first formulation of SequoiaOA, developing its mathematical model and engineering applications through algorithmic modelling and validation from the perspective of ecosystem complexity, self-regulation, and resilience in macro systems. Comparative experiments employing CEC2017 and CEC2022 benchmark functions demonstrate the algorithm's effectiveness through benchmarking against six established metaheuristic algorithms. SequoiaOA achieved superior performance in over 40 % of test functions, outperforming competitors in terms of mean values and variance of objective function measures. Furthermore, its efficacy in addressing real-world multi-constrained engineering challenges was validated through eight engineering design problems. Additional modelling and application experiments in UAV path planning underscore its practical applicability to trajectory optimization tasks. The in-depth discussions reveal that SequoiaOA possesses significant potential for future enhancements and demonstrates broad suitability for diverse optimization problems. The implementation code is available in the Appendix.