Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Abstract

In order to improve the global optimization of particle swarm optimization (PSO), enhance the performance of PSO in dealing with these complex, highdimensional, multimodal optimization problems, and furthermore, promote the optimization effect in reliability optimization applications, a direct position updating-based trying-mutation PSO (DTPSO) was proposed. In this algorithm, the direct position updating strategy and trying-mutation strategy were designed, which could effectively maintain the population diversity, balance the exploitation and exploration, and increase the probability of obtaining global optimal solution. After being verified and compared by nine complex test functions, the rationality of algorithm design and the excellent global optimization performances were proved. In the reliability optimization, the reliability redundancy allocation and reliability allocation were optimized by different advanced improved algorithms, the comparison results proved the stability and optimization performance of DTPSO. Due to its stable and efficient global optimization performance, it was concluded the proposed DTPSO could provide effective technical support for practical applications such as reliability optimization.

Optimization of polydopamine coating process for poly lactic acid‐based 3D printed bone plates using machine learning approaches

“…It exhibits fast convergence rates and does not necessitate the use of derivative information for the objective function. It is capable of addressing high dimensional problems that involve variables and constraints as it enables the generated solutions to conform to the constraints imposed by the optimization problem 54–56 . Unfortunately, it also has some disadvantages: (1) Functions with multiple local extremes may result in falling into a local extreme and obtaining an incorrect outcome.…”