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Nikola Ivkovic, Robert Kudelic and Marin Golub
Ant colony optimization (ACO) is a well-known class of swarm intelligence algorithms suitable for solving many NP-hard problems. An important component of such algorithms is a record of pheromone trails that reflect colonies? experiences with previously ...
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Michalis Mavrovouniotis, Maria N. Anastasiadou and Diofantos Hadjimitsis
Ant colony optimization (ACO) has proven its adaptation capabilities on optimization problems with dynamic environments. In this work, the dynamic traveling salesman problem (DTSP) is used as the base problem to generate dynamic test cases. Two types of ...
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Sílvia de Castro Pereira, Eduardo J. Solteiro Pires and Paulo B. de Moura Oliveira
A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keepin...
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Pavel V. Matrenin
Planning tasks are important in construction, manufacturing, logistics, and education. At the same time, scheduling problems belong to the class of NP-hard optimization problems. Ant colony algorithm optimization is one of the most common swarm intellige...
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Shuo-Tsung Chen, Tsung-Hsien Wu, Ren-Jie Ye, Liang-Ching Lee, Wen-Yu Huang, Yi-Hong Lin and Bo-Yao Wang
Recommended travel itinerary planning is an important issue in travel platforms or travel systems. Most research focuses on minimizing the time spent traveling between attractions or the cost of attractions. This study makes four contributions to recomme...
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