The Combination of Ant Colony Optimization (ACO) and Tabu Search (TS) Algorithm to Solve the Traveling Salesman Problem (TSP)

Dewantoro, Rico Wijaya; Sihombing, Poltak; Sutarman, Sutarman

doi:10.1109/elticom47379.2019.8943832

Cited by 22 publications

(14 citation statements)

References 11 publications

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“…In these applications, the city nodes in the graph represent, for instance, customers, soldering points, or DNA fragments, and each city pair or the distance represents the traveling time duration or cost, or the measurement between DNA fragments. Corresponding algorithms for coping with TSP include reinforcement learning [50], simulated annealing [51], genetic algorithm [52], ant colony [53], tabu search [54], or some mixed ones [124][125][126].…”

Section: Traveling Salesman Problemmentioning

confidence: 99%

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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Section: Traveling Salesman Problemmentioning

confidence: 99%

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

et al. 2022

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“…The closer the distance between the food, the less evaporation will occur, increasing the amount of pheromones. The more pheromones, the ants will follow the footprints [9]. The MPPT control flowchart using the ACO algorithm is shown in Figure 1.…”

Section: Mppt Simulation Using Aco and Pso Algorithmsmentioning

confidence: 99%

Optimizing Maximum Power Point Tracking on Photovoltaic Arrays using Ant Colony Optimization and Particle Swarm Optimization Algorithms

Hasan¹,

Suyono²,

Lomi³

2022

JSAE

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Pembangkit listrik tenaga surya pada umumnya tidak dapat menghasilkan daya maksimal sendiri. Karakteristik tegangan fotovoltaik (PV) umumnya mengikuti tegangan baterai atau beban yang terhubung langsung ke PV. Secara umum, tidak semua bagian modul PV menerima penyinaran atau intensitas cahaya yang seragam, sehingga produksi daya tidak optimal dan menyebabkan kasus multi-puncak. Sistem Maximum Power Point Tracking (MPPT) diperlukan untuk mengoptimalkan produksi listrik PV. Namun dalam banyak metode masih ditemukan beberapa kasus terjebak pada puncak lokal dan waktu konvergensi yang lama. Makalah ini mengkaji dan membandingkan kinerja dari dua metode pelacakan, yaitu Ant Colony Optimization (ACO) dan Particle Swarm Optimization (PSO). Hasil percobaan menunjukkan bahwa algoritma ACO memberikan efisiensi rata-rata keseluruhan yang lebih baik daripada PSO,

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“…The ACO algorithm is a heuristic algorithm inspired by the actual foraging behavior of ant colony [41], and it is often used to tackle path planning problems in TSP model because of its strong robustness, easy implementation, and convenient combination with other algorithms to improve performance [42]. However, the traditional ACO algorithm only considers the pheromone in the current state and neglects the influence of pheromones in subsequent states.…”

Section: B Path Planning Algorithmmentioning

confidence: 99%

A Hierarchical Information Acquisition System for AUV Assisted Internet of Underwater Things

Qin

Wang

et al. 2020

IEEE Access

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He holds 12 patents, and has authored or co-authored more than 100 technical papers in the behavior of computer network, P2P network and cognitive networks. He has serves as a reviewer of IEICE Transactions on Communications, Digital Signal Processing, Chinese Physics Letters, Chinese Journal of Electronics, Chinese Journal of Computer Science & Technology, Chinese Journal of Aeronautics and so on. His current research interests include complex systems theory and its applications to the optimization and information sharing of the Internet, Internet of Things and ubiquitous network, cognitive networks and Cyber-Physical Systems.

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The Combination of Ant Colony Optimization (ACO) and Tabu Search (TS) Algorithm to Solve the Traveling Salesman Problem (TSP)

Cited by 22 publications

References 11 publications

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

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

Optimizing Maximum Power Point Tracking on Photovoltaic Arrays using Ant Colony Optimization and Particle Swarm Optimization Algorithms

A Hierarchical Information Acquisition System for AUV Assisted Internet of Underwater Things

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