Abstract
This paper presents an enhanced and improved particle swarm optimization (PSO) approach to overcome reliability-redundancy allocation problems in series, series-parallel, and complex systems. The problems mentioned above can be solved by increasing the overall system reliability and minimizing the system cost, weight, and volume. To achieve this with these nonlinear constraints, an approach is developed based on PSO. In particular, the inertia and acceleration coefficients of the classical particle swarm algorithm are improved by considering a normal distribution for the coefficients. The new expressions can enhance the global search ability in the initial stage, restrain premature convergence, and enable the algorithm to focus on the local fine search in the later stage, and this can enhance the perfection of the optimization process. Illustrative examples are provided as proof of the efficiency and effectiveness of the proposed approach. Results show that the overall system reliability is far better when compared with that of some approaches developed in previous studies for all three tested cases.
Highlights
With the continuous advance of technology and increasing complexity of industrial systems, it has become imperative for all production processes to perform adequately during their designed life cycle
Two approaches have been commonly used by designers to achieve the desired system reliability. e first is increasing the component reliability and the second is dividing the system into multiple subsystems and using redundant components with the same or less reliability for different subsystems. e first approach is quite expensive, and the required reliability improvement may fail to be realized even when the most reliable components are used. e second approach involves using a combination of optimal redundant components
Classical/traditional particle swarm optimization (PSO) algorithm has flaws: it leads to premature convergence phenomenon, which cannot perform a good global search and fall into the local solution convergence situation, and it becomes hard to adapt to complex nonlinear optimization problems
Summary
With the continuous advance of technology and increasing complexity of industrial systems, it has become imperative for all production processes to perform adequately during their designed life cycle. E number of cuckoo search algorithm parameters seems to be less than GA and particle swarm optimization.
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