Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm. Moreover, the clothing style recognition model based on a multilabel classification algorithm and the clothing recommendation system based on a decision tree algorithm are tested by building simulation experiments and combining neural network technology. Finally, the application of the decision tree algorithm and DL theory in clothing recommendation design is studied through the literature collection method. The research focus is to realize the recognition of clothing through decision tree algorithm and DL method to achieve the intelligent recommendation of clothing style. The results show that: (1) the neural network technology in DL theory can realize efficient recognition and classification of clothing style by automatically extracting image features and combining with a multilabel classification algorithm. (2) The decision tree algorithm can make an initial recommendation according to users’ style preferences, then make implicit recommendations through user retrieval, browsing, and other operations, and make dynamic clothing style recommendations to users. (3) When the neural network based on a multilabel classification algorithm is trained, the precision, recall rate, and F1 values are 0.73, 0.43, and 0.55, respectively. (4) After using the clothing recommendation system based on the decision tree algorithm, the subjects’ average satisfaction is 86.25%, indicating that this system can give users a better clothing recommendation experience. This exploration aims to provide a crucial reference for further improving the quality of clothing recommendation services. It has important theoretical significance and practical value for the development of artificial intelligence in the field of fashion design, and is expected to provide a reference for the development of bionics.

Section: Methodsmentioning

confidence: 99%

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning

Yang¹

2022

“…Rule 2 is formulated mathematically as follows:

where C and S are 2 positive numbers; the previous is named as the cognitive accelerated co-efficient, and the final is named as the social accelerated co-efficient. At this point, p i , j represents the i th bird optimum preceding place and g j signifies the optimum previous swarm place [ 20 ].…”

Section: Proposed Modelmentioning

confidence: 99%

Fuzzy Cognitive Maps with Bird Swarm Intelligence Optimization-Based Remote Sensing Image Classification

Hilal

Alsolai

Al‐Wesabi

et al. 2022

Remote sensing image (RSI) scene classification has become a hot research topic due to its applicability in different domains such as object recognition, land use classification, image retrieval, and surveillance. During RSI classification process, a class label will be allocated to every scene class based on the semantic details, which is significant in real-time applications such as mineral exploration, forestry, vegetation, weather, and oceanography. Deep learning (DL) approaches, particularly the convolutional neural network (CNN), have shown enhanced outcomes on the RSI classification process owing to the significant aspect of feature learning as well as reasoning. In this aspect, this study develops fuzzy cognitive maps with a bird swarm optimization-based RSI classification (FCMBS-RSIC) model. The proposed FCMBS-RSIC technique inherits the advantages of fuzzy logic (FL) and swarms intelligence (SI) concepts. In order to transform the RSI into a compatible format, preprocessing is carried out. Besides, the features are produced by the use of the RetinaNet model. Besides, a FCM-based classifier is involved to allocate proper class labels to the RSIs and the classification performance can be improved by the design of bird swarm algorithm (BSA). The performance validation of the FCMBS-RSIC technique takes place using benchmark open access datasets, and the experimental results reported the enhanced outcomes of the FCMBS-RSIC technique over its state-of-the-art approaches.

“…the-Art Algorithm. To further verify the performance of the ICSO algorithm, DMSDL-QBSA which is a state-of-the-art algorithm proposed in the literature [18] is also used to compare with the ICSO algorithm in this section. In order to make the experimental comparison fairer and more reasonable, the parameter settings of the ICSO algorithm are the same as those of DMSDL-QBSA, that is, the population size is set to 30, and the maximum number of iterations is set to 100000.…”

Section: Experimental Comparison Between Icso and A State-of-mentioning

confidence: 99%

“…Therefore, it has a wide range of applications [ 15 , 16 ]. At present, it has been applied in optimization calculation [ 17 , 18 ], workshop scheduling [ 19 , 20 ], image engineering [ 21 ], network structure optimization [ 22 ], vehicle routing problem [ 23 ], control of teleoperating systems, and other fields [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems

Liang

Wang

2022

To solve the premature convergence problem of the standard chicken swarm optimization (CSO) algorithm in dealing with multimodal optimization problems, an improved chicken swarm optimization (ICSO) algorithm is proposed by referring to the ideas of bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) algorithm. First, in order to improve the depth search ability of the algorithm, considering that the chicks have the weakest optimization ability in the whole chicken swarm, the replication operation of BFA is introduced. In the role update process of the chicken swarm, the chicks are replaced by the same number of chickens with the strongest optimization ability. Then, to maintain the population diversity, the elimination-dispersal operation is introduced to disperse the chicks that have performed the replication operation to any position in the search space according to a certain probability. Finally, the PSO algorithm is integrated to improve the global optimization ability of the algorithm. The experimental results on the CEC2014 benchmark function test suite show that the proposed algorithm has good performance in most test functions, and its optimization accuracy and convergence performance are also better than BFA, artificial fish swarm algorithm (AFSA), genetic algorithm (GA), and PSO algorithm, etc. In addition, the ICSO is also utilized to solve the welded beam design problem, and the experimental results indicate that the proposed algorithm has obvious advantages over other comparison algorithms. Its disadvantage is that it is not suitable for dealing with large-scale optimization problems.