A computational study on ant colony optimization for the traveling salesman problem with dynamic demands

Oliveira, Sabrina; Bezerra, Leonardo C. T.; Stützle, Thomas; Dorigo, Marco; Wanner, Elizabeth F.; Souza, Sérgio R. de

doi:10.1016/j.cor.2021.105359

Cited by 19 publications

(3 citation statements)

References 63 publications

(178 reference statements)

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“…ACO is a bionic algorithm inspired by the behavior of ants foraging in nature [25]- [26]. In nature, during the ant foraging process, the ant colony can always find an optimal path from the nest to the food source [27]- [28]. ACO was first used to solve TSP problems and has shown great advantages due to its distributed nature, robustness and ease of integration with other algorithms.…”

Section: Tsp Problem and Aco Algorithmmentioning

confidence: 99%

Learning Path Planning Algorithm Based on Career Goals and Artificial Intelligence

Shi

Zhang

et al. 2022

Int. J. Emerg. Technol. Learn.

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With the development of the Internet, various forms of learning resources continue to flood the public, especially the promotion of video learning plat-forms. More and more people use their spare time to learn what they need. However, there is a general lack of intelligence in online learning platforms at present, which greatly reduced the utilization of online learning platforms and their educational advantages. The innovation of this paper is proposing and explaining that with the integration of big data, online education and ar-tificial intelligence, the contradiction of online education has turned into one between the lack of intelligence in online education and the demand of users. To solve this contradiction, this paper researches from the perspective of an algorithm. Course recommendation is the core algorithm of online ed-ucation. However, the current recommendation algorithm based on collabo-rative filtering has the disadvantages of cold start and useless recommenda-tion content. In this paper, Apriori and ACO algorithms in artificial intelli-gence are studied, and the proposal of an algorithmic framework named Posi-tion-Apriori-ACO brings forth new ideas in solving online education prob-lems. The Position-Apriori-ACO algorithm can effectively carry out course recommendation and learning path planning, and also provides a research di-rection for the intelligent development of online education.

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Section: Tsp Problem and Aco Algorithmmentioning

confidence: 99%

Learning Path Planning Algorithm Based on Career Goals and Artificial Intelligence

Shi

Zhang

et al. 2022

Int. J. Emerg. Technol. Learn.

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“…Dorigo et al [22], using the behavior of ants to find food, have introduced the ant algorithm. For a comprehensive study about the ACO algorithm, we refer to literature [23,24]. Now, by considering the non-linear nature of the upper limb rehabilitation robot system and the conditions of uncertainty and disturbances, the coefficients of the classic PID controller should be adjusted to ensure system stability and high tracking accuracy.…”

Section: Introductionmentioning

confidence: 99%

Development and Control of an upper Limb Rehabilitation Robot via Ant Colony Optimization -PID and Fuzzy-PID Controllers

Mirrashid

Alibeiki

Rakhtala

2022

IJE

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“…This study builds a model for cold chain logistics and route optimization that has low transportation, carbon, and refrigeration costs (16) . An ant colony optimization algorithm has been introduced for the travelling salesman problem with dynamic demands (17) . Travelling salesman problem has also been solved using a new modified genetic algorithm with a new special initialization of population (18) .…”

Section: Introductionmentioning

confidence: 99%

Solving a Multi-Conveyance Travelling Salesman Problem using an Ant Colony Optimization Method

Mondal¹,

Srivastava²

2022

IJST

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Objectives: A well-known NP-complete problem is the travelling salesman problem (TSP). It has numerous engineering and scientific applications. In this article, we have proposed a multi-conveyance TSP where different conveyances are present to travel from one city to another city. This is an extension to classical TSP. In this TSP, the salesman visits all the cities only once during his/her tour, using different conveyances to travel from one city to another. The cost of travelling between cities using various modes of conveyance varies. The objective of this research is to find the minimum cost tour using an ant colony optimization (ACO) based approach by satisfying the constraints of the proposed multi-conveyance TSP. Method: The considered TSP has been solved using a novel ACO technique. The proposed ACO is adopted with the Roulette-wheel selection and "tuning solution" techniques. We have used a few benchmark datasets from TSPLIB to check the effectiveness of the proposed algorithm. The experimental findings for a few benchmarks TSP instances show that almost always, the proposed ACO is able to find a better result. Then, we used some redefined and randomly generated datasets for experiments. The experimental outcomes for various input datasets are also very encouraging. Findings: The goal of the proposed TSP is to find a complete tour with a minimum cost without exceeding the total travel cost and total travel time. Thus, there are two novel constraints in the classical TSP. Novelty: A unique aspect of the proposed research is the use of several conveyance facilities and a fixed total travel time and cost. This is new, as these three factors are integrated into a single TSP model.

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A computational study on ant colony optimization for the traveling salesman problem with dynamic demands

Cited by 19 publications

References 63 publications

Learning Path Planning Algorithm Based on Career Goals and Artificial Intelligence

Learning Path Planning Algorithm Based on Career Goals and Artificial Intelligence

Development and Control of an upper Limb Rehabilitation Robot via Ant Colony Optimization -PID and Fuzzy-PID Controllers

Solving a Multi-Conveyance Travelling Salesman Problem using an Ant Colony Optimization Method

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