Many-Objective Optimization of Railway Alignments With Strengthened Pareto Dominance Analysis

Railway alignment design is a crucial but difficult task that should trade off many objective factors. Currently, although several Multi-objective Intelligent Alignment Optimization (M-IAO) methods have been proposed, previous methods may still be limited by low convergence performance with more than three objectives. In response, a Many-objective Intelligent Alignment Optimization (Ma-IAO) method is presented in this paper. First, a six-objective Ma-IAO model is built considering three kinds of objective factors in railway design, namely economic, geologic and ecologic factors. Then, a Particle Swarm Optimization with Strengthened Pareto Dominance Analysis (SPDA-PSO) is developed to solve the model. Two types of external archives are primarily designed to store and update nondominated solutions during optimization with a specifically-proposed strengthened pareto dominance criterion (known as LC-dominance). Afterward, two elite selection operators are devised to search for Pareto corner and knee points within external archives as the best particle individuals for guiding SPDA-PSO evolution. Lastly, the proposed method has been applied to a complex real-world railway example. Through comparisons with a contemporary M-IAO method and the manual work of human designers, its effectiveness is confirmed via detailed data analyses.