Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics

The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is used to maximize coverage in WSN by converting regional monitoring into point monitoring utilizing the discretization method in WSN. In the experiments, the ICS-PSO-OBL with the standard CS and three CS variants (MACS, ICS-2, and ICS) are utilized to execute the simulation experiment under different numbers of nodes (20 and 30, respectively). The experimental results reveal that the optimized coverage of ICS-PSO-OBL is 18.36%, 7.894%, 15%, and 9.02% higher than that of standard CS, MACS, ICS-2, and ICS when the number of nodes is 20. Moreover, it is 16.94%, 9.61%, 12.27%, and 7.75% higher when the quantity of nodes is 30, the convergence speed of ICS-PSO-OBL, and the distribution of nodes is superior to others.

Section: Introductionmentioning

confidence: 99%

An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks

Yang,

Xiang,

Kang

et al. 2024

“…FJSSP is intensely NP-hard 8 , and is a further complicated form of JSSP. Accordingly, the metaheuristic algorithms are the vital substitute to resolve this kind of problems which give an appropriate solution within a satisfactory time period 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem

Abdulqader

Ali

2023

Artificial fish swarm algorithm (AFSA) is one of the critical swarm intelligent algorithms. In this paper, the authors decide to enhance AFSA via diversity operators (AFSA-DO). The diversity operators will be producing more diverse solutions for AFSA to obtain reasonable resolutions. AFSA-DO has been used to solve flexible job shop scheduling problems (FJSSP). However, the FJSSP is a significant problem in the domain of optimization and operation research. Several research papers dealt with methods of solving this issue, including forms of intelligence of the swarms. In this paper, a set of FJSSP target samples are tested employing the improved algorithm to confirm its effectiveness and evaluate its execution. Finally, this paper concludes that the enhanced algorithm via diversity operators has discrepancies about the initial AFSA, and it also provided both sound quality resolution and intersected rate.

“…Combinatorial optimization algorithms allow solving a large number of practical problems, such as, for example, the traveling salesman problem, assignment problems, scheduling problems, building decision trees, the dimension of which can reach exponential. The authors of the articles [15][16][17] offer an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms in optimal content search problems have a wide range of tasks, including tasks related to model training, are an alternative solution for deep learning of a neural network, and solve the problems of model retraining 16 .…”

Section: Introductionmentioning

confidence: 99%

Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods

Chepurnenko,

Kondratieva,

Al-Wali

2023

Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of the study is the generated data sets obtained on the basis of theoretical stress relaxation curves. Tables of initial data for training models for all samples are presented, a statistical analysis of the characteristics of the initial data sets is carried out. The total number of numerical experiments for all samples was 346020 variations. When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. As a result of the study, intelligent models were developed to determine the rheological parameters of polymers included in the generalized non-linear Maxwell-Gurevich equation (initial relaxation viscosity, velocity modulus) using generated data sets for the EDT-10 epoxy binder as an example. Based on the results of testing the models, the quality of the models was assessed, graphs of forecasts for trainees and test samples, graphs of forecast errors were plotted. Intelligent models are based on the CatBoost algorithm and implemented in the Jupyter Notebook environment in Python. The constructed models have passed the quality assessment according to the following metrics: MAE, MSE, RMSE, MAPE. The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. Model performance estimates obtained during testing are valid.