Abstract

The Ivy Optimization Algorithm (IVY) is a novel swarm intelligence optimization algorithm that simulates the phototropic growth mechanism of plants. To comprehensively improve the overall optimization performance, this paper proposes an enhanced Ivy Optimization Algorithm (LSIVY) integrating improved Logistics chaotic mapping, sine–cosine operator, and adaptive t-distribution mutation strategy. Firstly, an improved cascaded Logistics chaotic mapping is used for population initialization. The double arcsine transformation improves the ergodicity and uniformity of chaotic sequences, so that initial solutions are distributed more evenly in the search space, population diversity is enhanced, and premature convergence is suppressed. Secondly, the sine–cosine operator is embedded into the position update mechanisms of IVY growth, climbing, and propagation evolution. Nonlinearly decreasing control parameters realize adaptive switching between global exploration and local exploitation and accelerate convergence. Thirdly, an adaptive t-distribution mutation strategy is designed to dynamically adjust mutation intensity according to the iteration cycle and implement directional perturbation at the optimal solution position. It combines the large-scale exploration advantage of the Cauchy distribution and the local fine search merit of the Gaussian distribution, which significantly improves the ability to escape from local optima. Comparative experiments with eight mainstream metaheuristics (DE, WOA, GWO, HHO, DBO, MBWO, AOO, native IVY) are conducted with 30 independent runs on 30-dimensional CEC 2014 (30 test functions) and CEC 2020 (10 composite functions). Quantitatively, LSIVY achieves 20~30 orders of magnitude higher optimization accuracy than standard IVY on unimodal functions, and its average standard deviation across all benchmarks drops by 4–6 orders of magnitude. LSIVY ranks first on all CEC 2020 composite functions, reducing over 30% of iterations compared with native IVY. Three classical constrained mechanical design problems (three-bar truss, cantilever beam, pressure vessel) are adopted for engineering verification. In the pressure vessel case, the average manufacturing cost of LSIVY is reduced by 9.2% against standard IVY, and the standard deviation of three engineering cases decreases by 2–3 orders on average, demonstrating remarkable robustness. The proposed algorithm not only improves the theoretical system of plant-inspired swarm intelligence algorithms but also has great application prospects in mechanical structure lightweight design, industrial equipment cost optimization, and other practical engineering fields.

IPC Classification

Keywords