Fig Tree Optimization Algorithm: A Case Study on Adaptive Cruise Control

This paper presents the Fig Tree Optimization (FTO) algorithm as a novel nature-inspired metaheuristic for solving complex optimization problems, focusing on automotive applications. Inspired by root sucker propagation and seed dispersal in fig trees, FTO executes these two search strategies in a parallel and overlapping manner, resulting in rapid convergence toward solutions near the global optimum. The effectiveness of FTO is systematically validated through extensive experiments on 28 benchmark functions, including 16 classical benchmark functions and 12 standard benchmark functions. Its performance was compared against four state-of-the-art algorithms: Growth Optimizer (GO), Puma Optimizer (PO), Success-Based Optimization Algorithm (SBOA), and Gray Squirrel Food Search Algorithm (GSFA). The results consistently demonstrate the superiority of FTO in terms of accuracy, convergence speed, and solution quality. The practical application of FTO was evaluated by tuning a PID controller in an Adaptive Cruise Control (ACC) system and comparing its performance with the PO algorithm across three modes. In the first mode, with computational time similar to PO, the average best cost was 4-fold higher, making it suitable for energy-limited scenarios with lower accuracy requirements. In the second mode, a 33% longer runtime yielded a 1.5-fold performance improvement, fitting cases with constrained energy and time but requiring high accuracy. In the third mode, a 143% increase in runtime (reducible to 33% in parallel) enhanced overall efficiency by 8.15-fold. This mode is appropriate when ample resources are available and high accuracy is required. FTO is an efficient tool for both benchmark and real-world challenges.