An intelligent optimization algorithms library for assembly planning

The objective of assembly sequence planning and optimization is to obtain the optimal sequences with the lowest cost in the given constraints, which is known as a NP-complete problem. Intelligent optimization algorithms are proved to be efficient in dealing with such combinatorial problems. Generally, the performances of various algorithms according to various specific problems are different, and no one can be competent for all the situations. This paper presents a framework and its implementation of an intelligent optimization algorithms library including several intelligent algorithms such as genetic algorithm (GA), simulated annealing (SA), particle swarm optimization algorithm (PSO), etc, and a series of evaluation indices such as quality of solution, efficiency, reliability, etc. Based on the evaluation indices and the algorithm features, an algorithm advisor is implemented with the consideration of the characteristics of assembly sequence planning problem to help the users choose appropriate intelligent algorithms. An illustrative example is given to demonstrate and evaluate the performance of the algorithm library. Copyright