Improving Ant Colony Optimization efficiency for solving large TSP instances

Skinderowicz, Rafał

doi:10.1016/j.asoc.2022.108653

Cited by 57 publications

(19 citation statements)

References 39 publications

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“…Since the nature of ACBIO is a positive feedback mechanism, the suboptimal solution will hold a greater advantage if it is obtained first, causing the algorithm to focus on the better candidate solution earlier. This mechanism reduces the population diversity and limits the global optimization capability of the algorithm (Chen et al, 2021;Skinderowicz, 2022). As shown in Figure 7A and the left panel of of ACBIO as the reference of the suboptimal solution, the average solution of DGA is different by 10, 15, and 38% to the suboptimal solution, respectively.…”

Section: Dynamic Genetic Algorithm With Ant Colony Binary Iterative O...mentioning

confidence: 99%

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

Liu

Zhang

et al. 2022

Front. Plant Sci.

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Section: Dynamic Genetic Algorithm With Ant Colony Binary Iterative O...mentioning

confidence: 99%

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

Liu

Zhang

et al. 2022

Front. Plant Sci.

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“…The firefly load-balancing algorithm was proposed by Manisha et al It reduces the computational cycles and the degree of load imbalance while exhibiting better working performance [ 22 ]. Both the genetic ant colony algorithm proposed by Cheng Cheng et al [ 23 ] and the ACO focusing algorithm proposed by Skinderowicz Rafał [ 24 ] improved the ant colony algorithm to a new level and obtained better computational performance. Tang Bo et al proposed the idea of applying genetic algorithms to the mapping process of grid blocks and processors and then performing intelligent allocation [ 2 ], but genetic algorithms have more space for optimization than other algorithms.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

Sun

et al. 2022

Biomimetics

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With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing.

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“…Tuani et al [48] adopted an adaptive method for multi-phase ant colony population and employed the control parameters of evolutionary ACO algorithm to locate the approximate optimal solutions. Skinderowicz [22] proposed a new focused ACO, the core element of which is to control the difference between the newly constructed solution and the previously selected solution.…”

Section: Optimization Algorithms To Travel Route Planningmentioning

confidence: 99%

“…ese methodologies are mainly according to heuristic algorithms, such as A * [15][16][17], genetic algorithm (GA) [18], simulated annealing (SA) [19], particle swarm optimization (PSO) [20], and ACO [21]. ACO is a famous and popular pathfinding algorithm, which is used to solve the shortest path problem based on graph [22]. Many researchers have applied the ACOs on solving gtraveling salesman problems, scheduling problems [23] and vehicle routing planning problems [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

Sun

Chen

Jin

et al. 2022

Journal of Advanced Transportation

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With the swift development of tourism all around the world, it has become vital to improve the recommendation of useful travel information to tourists to assure their convenience and satisfaction. In this paper, we propose a novel multi-objective optimal travel route recommendation framework, which collects tourists’ travel trajectories from their mobile phone signaling data. Then, the proposed framework preprocesses the mobile signaling data to transform raw trajectories into tourists’ travel sequences. Subsequently, the framework finds the popular attractions and frequent travel routes from the travel pattern sequences by using a frequent pattern mining method. Finally, an improved ant colony optimization (ACO) algorithm with a novel extensible heuristic factor approach is adopted to search the multi-objective optimal travel routes according to the popularity of attractions and travel time of tourists. The experimental results indicate that the proposed framework is efficient in recommending multi-objective optimal travel routes considering tourists’ travel time and attractions’ popularity while ensuring that the recommended travel route is suitable.

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Improving Ant Colony Optimization efficiency for solving large TSP instances

Cited by 57 publications

References 39 publications

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

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