An improved ant colony optimization with an automatic updating mechanism for constraint satisfaction problems

Guan, Boxin; Zhao, Yuhai; Yuan, Li

doi:10.1016/j.eswa.2020.114021

Cited by 39 publications

(20 citation statements)

References 27 publications

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“…Based on (R, Q) ordering policy, Mostafa et al [ 39 ] showed the information sharing’s influence on inventory costs. With the development of meta-heuristic algorithms, several different meta heuristic algorithms have been used to solve the two-tier supply chain problem [ 40 ], improve the efficiency of the supply chain [ 41 ], solve the vehicle routing problem [ 42 ], and optimize the response time in medical treatment [ 43 ].…”

Section: Aco Algorithmmentioning

confidence: 99%

An improved ant colony optimization algorithm based on context for tourism route planning

Liang

Jiao

et al. 2021

PLoS ONE

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To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.

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Section: Aco Algorithmmentioning

confidence: 99%

An improved ant colony optimization algorithm based on context for tourism route planning

Liang

Jiao

et al. 2021

PLoS ONE

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“…Even if the first proof-of-concept application for the ACO was a traveling salesman problem (TSP), up to now the above algorithm was applied to many combinatorial optimization problems. For instance, it was applied to assignment problems [6][7][8], routing problems [9][10][11], scheduling problems [12,13]. Less known but equally efficient applications concern the resource-constrained project scheduling problem [14,15], flow shop scheduling [16], sequential ordering problem [17], and open shop scheduling problem [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Ant Colony Optimization with Warm-Up

Neroni

2021

Algorithms

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The Ant Colony Optimization (ACO) is a probabilistic technique inspired by the behavior of ants for solving computational problems that may be reduced to finding the best path through a graph. Some species of ants deposit pheromone on the ground to mark some favorable paths that should be used by other members of the colony. Ant colony optimization implements a similar mechanism for solving optimization problems. In this paper a warm-up procedure for the ACO is proposed. During the warm-up, the pheromone matrix is initialized to provide an efficient new starting point for the algorithm, so that it can obtain the same (or better) results with fewer iterations. The warm-up is based exclusively on the graph, which, in most applications, is given and does not need to be recalculated every time before executing the algorithm. In this way, it can be made only once, and it speeds up the algorithm every time it is used from then on. The proposed solution is validated on a set of traveling salesman problem instances, and in the simulation of a real industrial application for the routing of pickers in a manual warehouse. During the validation, it is compared with other ACO adopting a pheromone initialization technique, and the results show that, in most cases, the adoption of the proposed warm-up allows the ACO to obtain the same or better results with fewer iterations.

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“…At first, some cache segments are locked (ie, loaded into knapsack) in advance and they are no longer removed from this knapsack. At this stage, ACO 10 is used to determine the number of cache segments locked in advance. Then for the rest cache segments, this paper devises an expectation efficiency model to evaluate each cache segment's potential.…”

Section: General Thoughtmentioning

confidence: 99%

0‐1 Knapsack problem driven resource scheduling in caching‐enabled network: A case study on sports video

Xu¹

2021

Internet Technology Letters

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In caching‐enabled network (CEN), the cache resources are very limited and expensive, thus it needs to make the exact scheduling for these cache resources. The cache resources in CEN are usually stored in the buffer/segment form, that is, with integer feature. Therefore, this paper plans to convert the cache resource scheduling into a 0‐1 knapsack problem (KP01) with a given total cache budget. For the solution procedure of KP01, it includes two stages. At first, some cache segments are locked in advance, where ant colony optimization is adopted for this. Then, the rest cache segments are evaluated in turn based on expectation efficiency, and some cache segments with high expectation efficiency value are locked. For the proposed resource scheduling strategy in CEN, this paper makes a detailed case study by considering a hot application, that is, sports video transmission, including two parts. On one hand, a concrete example is established to illustrate the correctness of cache resource scheduling. On the other hand, two baselines are added to prove the efficiency of cache resource scheduling by introducing lots of sports videos. All experiments are driven over NS3, and the results are considerably satisfactory.

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An improved ant colony optimization with an automatic updating mechanism for constraint satisfaction problems

Cited by 39 publications

References 27 publications

An improved ant colony optimization algorithm based on context for tourism route planning

An improved ant colony optimization algorithm based on context for tourism route planning

Ant Colony Optimization with Warm-Up

0‐1 Knapsack problem driven resource scheduling in caching‐enabled network: A case study on sports video

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