Hybrid Multiple-Objective Grey Wolf Algorithm Solving Multi-Objective Vehicle Routing Problem with Time Windows.

A multi-objective vehicle routing optimization model is established to minimize total cost and equilibrium degree for multi-objective vehicle routing problem with time windows, and a hybrid multi-objective grey wolf algorithm is proposed to solve the problem. Mainly design three strategies: (1) A new encoding and decoding method is designed to achieve the conversion of continuous grey wolf position vectors to discrete customer sequences. (2) Convergence and distribution indicators are used to select decision individuals. (3) Multiple deletion and insertion operators have been designed to implement local routing optimization. To demonstrate the effectiveness of the algorithm, some numerical examples in Solomon are used as examples to experimentally compare the proposed algorithm with MOIGA and improved ACO algorithms. Experimental results show that the hybrid multi-objective grey wolf algorithm proposed in this paper can find a better Pareto solution, and its performance is better than other evolutionary algorithm. [ABSTRACT FROM AUTHOR]