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The standard particle swarm optimization (PSO) algorithm is the boundary constraints of simple variables, which can hardly be directly applied in the constrained optimization. Furthermore, the standard PSO algorithm often fails to obtain the global optimal solution when the dimensionality is high for unconstrained optimization. Thus, an improved PSO-based extended domain method (IPSO-EDM) is proposed to solve engineering optimization problems. The core idea of this method is that the original feasible region is expanded in the constrained optimization which is transformed into the unconstrained optimization by combining the ergodicity of chaos optimization and the evolutionary variation to realize global search. In addition, to verify the effectiveness of the IPSO-EDM, an unconstrained optimization case study, four constrained optimization case studies, and one engineering example are investigated. The results indicate that the computational accuracy of the IPSO-EDM is comparable to that provided by the existing literature, and the computational efficiency of the IPSO-EDM is significantly improved. Meanwhile, this method has conspicuous global search ability and stability in engineering optimization.