A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

Tang, Jun; Liu, Gang; Pan, Qingtao

doi:10.1109/jas.2021.1004129

Cited by 385 publications

(117 citation statements)

References 114 publications

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“…In this paper, we study the recommendation algorithm that fits the business scenario, how the recommendation engine works. The optimization direction of the algorithm is given in conjunction with the actual problem [ 5 ]. The idea of solving the problem of cold start and user interest prediction in the music recommendation scenario is given, which is a guideline for the implementation in recommendation system engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user’s preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.

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Section: Introductionmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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“…Heuristic algorithms are a subset of the artificial intelligence field, which is popular in solving different optimization problems and is often used to solve task scheduling problems [28,29]. Common heuristic algorithms include ant colony optimization (ACO) [30], genetic algorithm (GA) [31], particle swarm optimization (PSO) [32], simulated annealing algorithm (SAA) [33], Grey Wolf Optimizer (GWO) [34], monarch butterfly optimization algorithm (MBO) [35], and so on.…”

Section: Heuristic Algorithm To Solve the Task Scheduling Problemmentioning

confidence: 99%

“…The ACO can search on a large scale. To improve its search process, it can have excellent exploration and development capabilities at the stage of generating the optimal solution [28].…”

Section: Heuristic Algorithm To Solve the Task Scheduling Problemmentioning

confidence: 99%

“…The probabilistic algorithm ACO was designed by Marco Dorigo to find an optimal path, inspired by the behavior of ants in the process of searching for food. The ACO algorithm has many excellent properties and is mainly used to solve task scheduling problems with complex solution set [28,30]. We improved the ACO algorithm to address large-scale task scheduling problems, and it is convenient to obtain the optimal task service strategy faster.…”

Section: Improved Ant Colony Algorithmmentioning

confidence: 99%

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A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Yin

et al. 2022

Sensors

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With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

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“…In this case, they are at great risk of falling into local areas, especially when tackling multimodal problems with many wide and flat local regions, whereas heuristic algorithms could search the solution space in different directions by maintaining a population of individual solutions. (3) Heuristic algorithms usually preserve inherent parallelism to accelerate the iteration [18,22]. Specifically, at least, during the optimization, the fitness evaluation of each individual solution, which is usually the most time-consuming part in heuristic algorithms, could be separately executed, let alone that some parallel techniques could be designed and embedded into heuristic algorithms to accelerate the search process.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

et al. 2022

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Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants.

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A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

Cited by 385 publications

References 114 publications

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

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