Weight-Specific-Decoder Attention Model to Solve Multiobjective Combinatorial Optimization Problems

Ye, Te; Zhang, Zizhen; Chen, Jinbiao; Wang, Jiahai

doi:10.1109/smc53654.2022.9945568

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our work, we mainly studied the multi-objective capacitated vehicle routing problem (MOCVRP) [20], where there are two contradictory objectives: one objective is the total tour length, and the other one is the tour length for the longest route. PMOCO [21] is the most state-of-the-art algorithm in solving MOCVRP.…”

Section: Introductionmentioning

confidence: 99%

A Neural Multi-Objective Capacitated Vehicle Routing Optimization Algorithm Based on Preference Adjustment

Wang,

Song,

Sun

et al. 2023

Electronics

View full text Add to dashboard Cite

The vehicle routing problem (VRP) is a common problem in logistics and transportation with high application value. In the past, many methods have been proposed to solve the vehicle routing problem and achieved good results, but with the development of neural network technology, solving the VRP through neural combinatorial optimization has attracted more and more attention by researchers because of its short inference time and high parallelism. PMOCO is the most state-of-the-art multi-objective vehicle routing optimization algorithm. However, in PMOCO, preferences are often uniformly selected, which may lead to uneven Pareto sets and may reduce the quality of solutions. To solve this problem, we propose a multi-objective vehicle routing optimization algorithm based on preference adjustment, which is improved from PMOCO. We incorporate the weight adjustment method in PMOCO that is able to adapt to different approximate Pareto fronts and to find solutions with better quality. We treat the weight adjustment as a sequential decision process and train it through deep reinforcement learning. We find that our method could adaptively search for a better combination of preferences and have strong robustness. Our method is experimented on multi-objective vehicle routing problems and obtained good results (about 6% improvement compared with PMOCO with 20 preferences).

show abstract

Section: Introductionmentioning

confidence: 99%