Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

Jooken, Jorik; Leyman, Pieter; Wauters, Tony; Causmaecker, Patrick De

doi:10.1016/j.cor.2022.106070

Cited by 4 publications

(1 citation statement)

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“…4 Currently, there are three common fitness allocation strategies: allocation strategies based on Pareto priority relationship ranking, assignment strategies based on random weight summation, and assignment strategies based on selective weights. [5][6][7] Among them, the Pareto ranking method cannot effectively reflect the density information around the individuals in its fitness assignment, so that it is difficult to ensure the diversity of the population and the fast convergence of the algorithm. Therefore, this method cannot achieve better results in solving high-dimensional multi-objectives.…”

Section: Introductionmentioning

confidence: 99%

Research on multi-objective flow shop scheduling optimization in supply chain environment based on Fuzzy Relevance Entropy Method

Luo,

Tan,

Zhu

et al. 2023

Advances in Mechanical Engineering

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For the multi-objective flow shop scheduling problem in the supply chain environment, this paper proposes the Fuzzy Relevance Entropy method (FREM) to solve the adaptive value assignment problem in the multi-objective optimization process of the supply chain environment by combining Fuzzy Information Entropy Theory (FIET) and Degree of Membership Function (DMF). Firstly, the uncertainty of each sub-objective of the ideal solution and Pareto solution of the objective is extracted using the Degree of Membership Function. Secondly, each solution is mapped into an affiliation degree fuzzy set and the information contained in the fuzzy set is reprocessed using Fuzzy Information Entropy Theory. Finally, the amount of information contained in the ideal solution solved by the Pareto method is used to guide the evolution of the Particle Swarm Optimization (PSO) algorithm, thus avoiding the traditional multi-objective optimization process of assigning weights to solve the fitness link. This paper combines both the Fuzzy Relevance Entropy method and the Stochastic Weight method with Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms to address the five-objective flow shop scheduling problem in the supply chain environment. Experimental results demonstrate that the proposed Fuzzy Relevance Entropy method effectively solves the multi-objective flow shop scheduling problem in the supply chain environment and achieves better optimization results compared to the Stochastic Weight method.

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