Multi - Strategy Adaptive Harris Hawks Algorithm and its Applications

In the research of pressure vessel optimization design, aiming at the problems of slow convergence speed and easy trapping in local optimum of the traditional Harris Hawks Optimization (HHO) algorithm in complex optimization problems, this paper proposes a multi - strategy improved HHO algorithm (CPHHO). Firstly, a dynamic energy attenuation mechanism is introduced. The global exploration and local development capabilities are balanced through a piecewise function (exponential decay + cosine modulation) and a random perturbation factor. Secondly, the swarm memory characteristics of the Particle Swarm Optimization (PSO) are integrated, and the individual historical best (pbest) and global best (gbest) are used to guide the search direction. Meanwhile, the chaotic Logistic map is designed to replace the random parameters to enhance the population diversity. In addition, the dynamic jump intensity adjustment based on fitness and the memory bank restart strategy are added to effectively avoid premature convergence. The proposed algorithm outperforms the standard HHO by 1–3 orders of magnitude in optimization accuracy when tested on 18 CEC2017 benchmark functions (covering unimodal, multimodal, and hybrid composition types), while also showing significantly enhanced stability over state-of-the-art peers. Finally, CPHHO is applied to solve the pressure vessel optimization design problem, verifying the engineering practicability of the algorithm.