SSGTA: A Novel Swap Sequence based Game Theory Algorithm for Traveling Salesman Problem

Shahadat, Abu Saleh Bin; Ayon, Safial Islam; Khatun, Most. Rokeya

doi:10.1109/iccit54785.2021.9689823

Cited by 1 publication

(3 citation statements)

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“…Supervised learning, reinforcement learning and game theoretic methods are introduced in learning methods, where supervised learning includes methods of B&B [69][70][71][72][73][74], sequence to vector [57,[75][76][77], GNN [78][79][80][81][82] and end-to-end architecture [83,84]. A few sources are relevant to reinforcement learning [85][86][87][88], and different types of games were demonstrated [89][90][91][92]. Another aspect mentioned was classic COPs [71,72,84,85,[93][94][95][96][97], where ILP, MIS, MC, MVC and TSP were introduced.…”

Section: Copsmentioning

confidence: 99%

“…Shahadat et al [91] regarded each strategy of the TSP problem as a player and iteratively updated them through competition and reinforcement to obtain the optimal strategy. Specifically, Shahadat et al first randomly initialized some strategies, each of which has a payoff equal to 0.…”

Section: Competitive Gamementioning

confidence: 99%

“…They obtained good solutions in a short time on small-scale MTSPs. Shahadat et al [91] created a competitive game among strategies over TSPs. At each step, two strategies were compared, and the winner was used as a demonstration to the loser.…”

Section: Traveling Salesman Problemsmentioning

confidence: 99%

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A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

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Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed.

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