Multi-ant colony optimization algorithm based on hybrid recommendation mechanism

Liu, Yifan; You, Xiaoming; Liu, Sheng

doi:10.1007/s10489-021-02839-9

Cited by 6 publications

(2 citation statements)

References 54 publications

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“…Due to these shortcomings, a series of improved ant colony algorithms have been proposed in recent years. Liu and others [18] proposed a heterogeneous multi-ant colony strategy, which can balance the convergence and diversity of the algorithm and help the ant colony to jump out of the local optimal solution; nevertheless, when the global optimal solution is found, it takes a long time to play the role of positive feedback. Wang and Han [19] search the adaptive optimization parameters using hybrid symbiotic organisms and introduced a local optimization strategy to improve the convergence speed of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Research on Operation and Maintenance Route Planning of Offshore Wind Farm Based on Multiagent

Sheng

Sun

et al. 2022

Wireless Communications and Mobile Computing

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Recently, the ant colony algorithm has been widely used in the field of path planning, which is a key technology required for ship operations in offshore wind farms to improve navigation efficiency and power generation. However, the ant colony algorithm has the defects of a long search time and stagnation in the early stage of the operation and maintenance path planning of offshore wind farms, and it easily falls into the problem of local optima; furthermore, the ant colony algorithm uses incremental construction to build a complete itinerary path, it takes a lot of time to search the path, and only the suboptimal solution is obtained. To address the above problems, in this paper, we propose a multi-agent-based operation and maintenance model for offshore wind farms. Specifically, the introduction of the heuristic factor can optimize the local optimal solution and make the ant colony algorithm clearer when searching for the target. Based on this, increasing the pheromone adjustment factor can eliminate the invalid path from searching and select a high-quality path. In addition, by integrating the genetic algorithm, it is possible to select, cross, and mutate to simulate the natural evolution process to search for the optimal solution, reduce the similarity of the paths constructed by the ant colony, reduce the probability of algorithm stagnation, improve the convergence speed, and improve the time efficiency and solution accuracy of the algorithm. Simulation experiments on a series of benchmark datasets show that the proposed GA-PACO algorithm achieves better performance in global search and path planning than the existing three algorithms.

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Section: Related Workmentioning

confidence: 99%

Research on Operation and Maintenance Route Planning of Offshore Wind Farm Based on Multiagent

Sheng

Sun

et al. 2022

Wireless Communications and Mobile Computing

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“…The personalized recommendation system can analyze the user's interest preferences according to the user's behavior habits, and recommend the content related to the user's interest to the user without the user's initiative to provide information. The excellent performance makes the recommendation system widely used in many fields [3][4]. At present, there are many commonly used recommendation methods.…”

Section: Introductionmentioning

confidence: 99%

Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

Zhou¹

2023

IJACSA

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The current recommendation technology has some problems, such as lack of timeliness, the contradiction between recommendation diversity and accuracy. In order to solve the problem of lack of timeliness, the time factor is introduced when constructing the self-preference model. The cold start problem in the collaborative filtering algorithm is solved by the hybrid similarity calculation method, and the potential preference model is constructed. The two are fused to obtain a hybrid recommendation algorithm to improve the recommendation performance of the algorithm. For the problem of multi-objective contradiction, the NNIA algorithm is used to further optimize the candidate results of mixed recommendation, and the final recommendation list is obtained. Through verification experiments, the results show that the recall rate and accuracy of the fused preference model are better than those of the non-fused model, and the accuracy is 9.57% and 8.23% higher than that of SPM and PPM, and the recall rate is 9.97% and 7.65% higher, respectively. CBCF-NNIA algorithm has high accuracy and diversity of recommendation, and can provide users with rich and diverse text content to meet their own needs.

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Research on UAV Path Planning Combined with Ant Colony and A*

Zhang

2023

Lecture Notes in Electrical Engineering

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Multi-ant colony optimization algorithm based on hybrid recommendation mechanism

Cited by 6 publications

References 54 publications

Research on Operation and Maintenance Route Planning of Offshore Wind Farm Based on Multiagent

Research on Operation and Maintenance Route Planning of Offshore Wind Farm Based on Multiagent

Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

Research on UAV Path Planning Combined with Ant Colony and A*

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