Engineering design problems often involve various requirements and complexities, which are treated as constraints in optimization problems. Constrained optimization problems (COPs) are widely studied, and particle swarm optimization (PSO) has become a popular method for solving them due to its simplicity and effective convergence. However, PSOs still face limitations, particularly in terms of search efficiency and high computation costs for optimal solutions in complex feasible areas or near constraint boundaries. This paper presents a novel particle swarm optimization algorithm named independent 2-group particle swarm optimization (I2GPSO). I2GPSO is based on the following ideas - a constraint handling method, a novel structure of particles and a novel local search operator. The constraint handling method uses the existing penalty function method. The structure of particles defines two particle groups that have original roles, efficiently enabling PSO to search globally and locally. The local search operator is introduced into one group and enables particles to intensively search near the good solutions obtained by particles so far. These novel approaches effectively reinforce the optimization efficiency of the PSO algorithm. The optimization capability and characteristics of I2GPSO is illustrated in engineering design problems. The results are compared with other state-of-the-art PSOs, and it is shown that the proposed algorithm possesses competitive search efficiency.