A Clonal Selection Algorithm for Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem

Rapanaki, Emmanouela; Psychas, Iraklis-Dimitrios; Marinaki, Magdalene; Marinakis, Yannis; Migdalas, Athanasios

doi:10.1007/978-3-030-13709-0_32

Cited by 4 publications

(1 citation statement)

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“…Literature [10] proposes an improved genetic algorithm with adaptive adjustment of crossover probability, which performs better in the path planning of driverless vehicles. Literature [11] proposed a parallel multi-start and multi-target clonal selection algorithm for solving multi-objective Energy Reduction multi-depot vehicle routing problem. These heuristic algorithms have their own advantages and disadvantages, but they all depend on some parameters, the setting of related parameter values greatly affects the algorithm's ability to solve problems, and can't flexibly cope with the variable path planning problems.…”

Section: Introductionmentioning

confidence: 99%

A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

2020

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With the thorough application and development of Intelligent Logistics System, the Path Planning Problem, which is one of the key problems of logistics scheduling, is accelerating to the direction of dynamics, diversification and complexity. In order to better adapt to the new changes, this paper proposes a path planning method based on improved SP-MCTS (Single-Player Monte Carlo Tree Search) algorithm. Unlike the previous heuristic algorithms, SP-MCTS does not completely depend on parameters, and can more effectively solve variable combinatorial optimization problems. Aiming at the problem of path planning, this paper enhances the simulation process of SP-MCTS by clonal selection algorithm, in order to improve the algorithm's accuracy of locating high-potential branches, and further improve the accuracy of the decisions made by SP-MCTS. Experiments show that the method proposed in this paper is less affected by the parameters and can better solve the changing path planning problem. Compared with the existing path planning methods and original SP-MCTS algorithm, it can solve the changeable path planning problem more stably, faster and more accurately. INDEX TERMS single player-monte carlo tree search; clonal selection algorithm; path planning

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Section: Introductionmentioning

confidence: 99%