Dynamic Topology Two-phase Particle Swarm Optimization Algorithm And Multi-state System Reliability Optimization

The implementation of supply-side structural reforms and accelerating the optimization and upgrading of industrial structure are the main thread running through the Thirteen Five Plan, which requires a number of high technical level enterprises to be developed, so as to promote the industry towards high-end level. Adhering to lean production, development concept of improving quality and performance is critical for enterprises to improve their competitiveness, enterprises need optimize all aspects of actual production and reliability optimization technology is the key technology for enterprises to improve the performance and robustness of products. Thus, seeking the efficient reliability optimization method for businesses is involving in huge economic benefits. Particle Swarm Optimization(PSO) algorithm is a typical swarm intelligence optimization algorithm which has been successfully used in the complex reliability optimization problems, but its own shortcoming of premature convergence reduces the accuracy of the optimization results. Therefore, the paper carries out research on improving particle swarm optimization algorithm and its application in actual reliability optimization.First, for single force rule easily makes standard particle swarm algorithm prematurely converge and fall into local optimum, the paticle swarm algorithm is improved from the perspective of information exchange mechanism between the particles,two-phase particle swarm optimization algorithm is proposed. Based on phase search strategy the search process is divided into two phases, force rules are correspondingly constructed. The performance of the proposed algorithm is tested by optimizing standard test functions, the test results are compared with other improved particle swarm algorithm,which verifies the the proposed algorithm has better optimization ability.Secondly, the effect of static population topology structure on the performance of two-phase particle swarm algorithm is studied, by selecting topologies of different feature structures, such as fully connected topology, ring topology and NW small world network topology, the changes of algorhthm’s population diversity and optimization performancein three population static topologies are analyzed, and the relationship between the characteristic of population topology structure and the performance of the algorithm is summed up, which laies the foundation for researching dynamic population topology structure suiting the two-phase particle swarm optimization algorithm.Moreover, in order to simulate the dynamic interaction behavior of drawing on the advantages and avoiding disadvantages between biological individuals, take groups of organisms which exhibite self-organization in the social behavior and the fittest phenomenon as the starting point, a dynamic population topology structure is studed which takes particles’ fitness values to drive the operation of adding edges and deleting and reconstructing nodes, by combining the population structure evolution and the evolutionary of algorithm in a parallel way, the dynamic topology two-phase particle swarm optimization algorithm is proposed.Finally, the optimization model of multistate system reliability optimization which focuses on redundancy allocation problem is established, using dynamic topology two-phase particle swarm optimization algorithm to solve reliability optimization problems of series parallel multi-state system and multi-state system with bridge structure.The optimization results show that on the premise of the system reliability requirements,the proposed algorithm can reduce the design costs of the systems.