Practical engineering optimization problems are almost constrained optimization problems and difficult to be solved effectively, therefore, how to handle these problems has attracted more and more attention. Particle swarm optimization (PSO) is one of the most popular algorithms in solving the complicated optimization problems due to its relatively strong global optimization capability and low requirement for computing resources. However, PSO is easy to converge prematurely like other swarm intelligence algorithms due to the loss of diversity among particles. This article proposes an adaptive switchover hybrid PSO framework with local search process (ASHPSO), which adaptively switches the optimization searching process between the standard PSO and the differential evolution (DE) modified by a full dimension crossover strategy to avoid the premature convergence problem. Moreover, a local search strategy is employed to improve the boundary search capability of PSO in consideration of the engineering problems characteristics. Experiments on 28 well-known benchmark functions, 5 engineering problems and a full vehicle multi-disciplinary optimization problem demonstrate the effectiveness of the proposed algorithm compared with other hybrid variants.

实际的工程优化问题几乎是约束性的优化问题，难以有效解决，因此，如何处理这些问题引起了越来越多的关注。粒子群优化（PSO）由于具有相对较强的全局优化能力和对计算资源的低要求，因此是解决复杂优化问题的最受欢迎算法之一。但是，由于粒子之间的多样性丧失，PSO像其他群体智能算法一样容易过早收敛。本文提出了一种具有本地搜索过程（ASHPSO）的自适应切换混合PSO框架，该算法可在标准PSO和通过全维交叉策略修改的差分演化（DE）之间自适应地切换优化搜索过程，从而避免过早收敛的问题。此外，考虑到工程问题的特点，采用局部搜索策略来提高PSO的边界搜索能力。通过对28个著名的基准函数，5个工程问题以及一个完整的车辆多学科优化问题进行的实验证明，与其他混合动力变体相比，该算法是有效的。