Swarm Intelligence Optimization Research Articles

Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy.

Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance. PSO-LSTM model leveraging PSO's efficient swarm intelligence and strong optimization capabilities is proposed in this article. The experimental results on six global stock indices demonstrate that PSO-LSTM effectively fits real data, achieving high prediction accuracy. Moreover, increasing PSO iterations lead to gradual loss reduction, which indicates PSO-LSTM's good convergence. Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. Furthermore, the impact of different retrospective periods on prediction accuracy and finding consistent results across varying time spans are. Conducted in the experiments.

Application of Immunological and Swarm Intelligence Learning-Based Algorithm for Industrial Grade Computer Sales Prediction

ABSTRACT This paper strives to raise the imitating effectiveness of radial basis function-based neural network (RNNet) through biological learning (BL) and swarm intelligence (SI) optimization algorithms. Latter, the artificial immune system (AIS) and particle swarm optimization (PSO) algorithms are utilized for RNNet to regulate. The proposed synthesis of AIS-inspired and PSO-inspired (SAIPS) algorithm incorporates the complementary development and prospecting abilities to realize optimized resolution. The attribute of population variation has shown high frequency to meet the global optimum to replace local optimum being restricted and outperforms in five standard nonlinear trial functions. The experimental results have represented that the consolidation of AIS-inspired and PSO-inspired algorithms is an outstanding approach and therefore a hybrid algorithm is proposed, which aims to obtain an expression that can cultivate optimum precision among related algorithms in this research. The algorithm then evaluates results from five standard inspections and an empirical industrial grade computer (IgC) sales prediction instance in Taiwan, which reveals that the proposed SAIPS algorithm exceeds the performance among related algorithms as well as the relevant auto-regressive integrated moving average (ARIMA) models in terms of accuracy and time spent.

Fuzzy Self-tuning Bees Algorithm for designing optimal product lines

The Product Line Design (PLD) problem is an NP-hard combinatorial optimization problem in marketing that aims at determining an optimal product line through which a firm can optimize a desired objective, like its profits or market share. Since the PLD problem has been proved to have high complexity in real-life applications, high-quality solutions have been detected by researchers who develop various optimization methods and test their performance. The Bees Algorithm (BA) is a successful swarm intelligent optimization algorithm which is based on the behavior of bees. The aim of this research is to develop and assess BA in the optimal PLD problem. In this effort, a set of fuzzy rules has been developed to autonomously compute parameters for each individual solution throughout the optimization process. The performance of two BA variants is compared with those of popular previous approaches, using both real and simulated data of customer preferences. The findings reveal that BA constitutes an enhanced alternative approach for designing optimal product lines.

Integrating knowledge-data-driven method to predict load-displacement curve on a trapdoor

Accurately predicting the overlying earth pressure of underground structures is a key point in the design and construction of underground structures. On the basis of the existing composite function model describing the load–displacement curve (LDC) and experimental data, a intergrating knowledge-data-driven method to predict LDC is proposed in this paper. The buried depth (H), trapdoor width (B), soil weight (γ) and internal friction angle (φ) are selected as multi-characteristic input variables. The initial modulus of arching a, the minimum soil arching ratio ρmin, the minimum soil arching ratio displacement ξmin and the ultimate soil arching ratio ρult are output variables. Two swarm intelligence optimization algorithms (i.e., sparrow search algorithm (SSA) and particle swarm optimization (PSO)) are used to optimize the parameters of the established generalized regression neural network (GRNN), and the knowledge-data cooperatively driven prediction of the LDC is realized. The results show that the GRNN model optimized by swarm intelligence algorithm has better prediction performance than the GRNN model. The LDC obtained from the output parameters of three GRNN models are compared with the results of the trapdoor experiments. The comparison results show that the LDC obtained by the GRNN model optimized by swarm intelligence algorithm are more consistent with the experimental results than that those obtained by GRNN model, and the prediction performance of the SSA-GRNN is slightly better than that of the PSO-GRNN.

EXPRESS: Tourism demand interval forecasting with an intelligence optimization-based integration method

Interval forecasting for tourism demand holds significant theoretical and practical insights. However, research on integrating social reviews into multi-source for interval prediction is still developing. To fill this research gap, this study proposes an integrated method for tourism demand interval prediction by combining multi-source data with a modified swarm intelligence optimizer. This method can extract essential intrinsic features from multi-source and select an appropriate probability density function to extend point predictions to initial prediction intervals, then further refine the initial prediction intervals to improve the prediction accuracy. Empirical studies on the tourism demand of Mount Siguniang and Jiuzhaigou validate the superior predictive capabilities of the proposed model. Experimental results demonstrate that (a) incorporating a multi-source dataset with social reviews significantly enhances the accuracy of the proposed model; (b) the modified transit search algorithm effectively balances the coverage and width of prediction intervals, thus improving the generalizability of the model.

Multiple strategies improved spider wasp optimization for engineering optimization problem solving

The Spider Wasp Optimization (SWO) algorithm is a swarm intelligence optimization technique inspired by the collective behaviors of social animals. This algorithm, designed to address optimization challenges, emulates the unique hunting, nesting, and mating behaviors of female spider wasps. It offers several advantages, including rapid search speed and high solution accuracy. However, when tackling complex optimization problems, it can encounter issues such as getting trapped in local optima, slow early convergence, and the need for manual adjustment of the “Trade-off Rate” (TR) parameter for different problems.To improve the performance and versatility of the SWO algorithm, a Multi-strategy Improved Spider Wasp Optimizer (MISWO) is proposed. Firstly, the Grey Wolf Algorithm is integrated into the initialization phase to enhance early convergence and improve the fitness of the initial population, thereby boosting the algorithm’s global optimization capabilities.Secondly, an adaptive step size operator and Gaussian mutation are introduced during the search phase to automatically adjust the search range at different optimization stages. This enhancement increases both the optimization accuracy and the algorithm’s ability to avoid local optima. The Trade-off Rate (TR) is dynamically selected to better accommodate a variety of problems. Finally, a dynamic lens imaging reverse learning strategy is employed to update optimal individuals, further improving the algorithm’s capacity to escape local optima. To validate the effectiveness of MISWO, it was tested on 23 benchmark functions and 7 engineering optimization problems, and compared with several state-of-the-art algorithms. Experimental results show that MISWO outperforms other algorithms in terms of optimization capability, stability, and adaptability across diverse problems.

Frigatebird optimizer: a novel metaheuristic algorithm

Abstract This paper proposes a new swarm intelligence optimization algorithm - the Frigate Bird Optimizer (FBO). The algorithm is inspired by the unique flight and foraging behaviors of frigatebirds. Its optimization process is divided into two stages: The first stage simulates frigatebirds harassing other seabirds to snatch food, and the search direction and radius of individuals have randomness and uncertainty, which is conducive to global exploration; The second stage simulates frigatebirds observing large fish driving small fish to leap out of the water and preying on them, and individuals tend to gather in the optimal search direction. By simulating the behaviors of frigatebirds under different survival strategies, the algorithm achieves extensive global search in the first stage and fine-tuned local optimization by learning information in the second stage. To evaluate its performance, 46 functions in the CEC2014 and CEC2017 benchmark test sets are selected as objective functions and compared with 9 state-of-the-art meta-heuristic algorithms. The results show that the FBO algorithm has higher performance, excellent iterative optimization ability and strong robustness, and can be applied to different optimization domains.

An improved grey wolf optimization algorithm based on bounded subpopulation re-search strategy

Abstract Swarm intelligence optimization algorithms are widely used in solving and optimizing complex problems in various industries due to their simple and effective structure, as well as ease of deployment and implementation. The paper puts forward a new grey wolf optimization (GWO) algorithm by using bounded grey wolf subpopulation re-search strategy to deal with problems of local optima and low precision in solving complex control, decision-making, and planning problems in intelligent optimization algorithms. Firstly, based on the traditional GWO algorithm model, the intelligent individuals, leadership selection, and location update methods of the basic algorithm are analyzed, and the basic process of grey wolf optimization is established. Secondly, by adopting three strategies including boundary reflection, secondary search, and particle swarm optimization update, a new update model and search mechanism are designed for the GWO algorithm, effectively solving the problem of the gray wolf optimization algorithm being prone to falling into local optima and premature convergence. Finally, the effectiveness and superiority of the proposed algorithm are tested through numerical simulation and comparative experiments.

GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.

Application of sparrow search swarm intelligence optimization algorithm in identifying the critical surface in slope-stability

The use of nature-inspired meta-heuristics to tackle complex optimization problems is steadily gaining popularity within a rapidly evolving world. Swarm intelligence (SI) optimization motivated by behavior of community-based organisms of flocks of birds, schools of fish, colonies of ants and bees performs the search through agents whose trajectories are primarily adjusted stochastically and sporadically deterministically, by golden rules drawn from Mother Nature. Each entity within the swarm is swayed by its individual ‘best’ and group’s ‘best’ position while moving randomly to converge to optimal through competition and cooperation. The sparrow search algorithm (SSA), developed by Xuea and Shen (Xue and Shen in Syst Sci Cont Eng 8:22–34, 2020) is a very recent SI approach that adopts the sparrow producer–scrounger model metaphorically for designing optimum searching strategies, inspired by the foraging, anti-predation behavior, and the overall group wisdom of sparrows. SSA has been experimented on some hard benchmark test functions to test its effectiveness and thereafter, applied in slope-stability problems in searching the critical failure surface. The objective function to be optimized is the factor of safety against failure given by Bishop's (Bishop in Geotechnique 5:7–17, 1955) simplified method. Results show SSA is a strong contender to bio-inspired methods like genetic algorithms, big-bang big-crunch, simulated annealing, and artificial bee colony algorithms. The study illustrates the flexibility, efficiency, and robustness of the methodology in function optimization.

Power Transformer Fault Diagnosis Based on Random Forest and Improved Particle Swarm Optimization–Backpropagation–AdaBoost

This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem of determining significant features in the dataset. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer backpropagation neural network (BPNN), which overcomes the challenge of determining hyperparameters in the model. Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO–BP–AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO–BP–AdaBoost model.

A hybrid sparrow optimization Kriging model and its application in geological modeling

With the proposal of intelligent mines, the demand for drilling is increasing daily. Therefore, it is particularly crucial to gather more geological data by interpolation of limited drilling data for subsequent three-dimensional geological modeling. In this paper, a hybrid sparrow optimization Kriging model (HSSA), in which chaos theory and Levy flight are integrated into the initial population update algorithm of the sparrow algorithm and the location update algorithm of the entrants, is proposed. Next, the golden sine optimization algorithm is introduced into the reconnaissance and early warning mechanism of the sparrow algorithm to further improve the accuracy and local escape ability. By the correlation optimization of the original sparrow algorithm, the speed and accuracy of swarm intelligence optimization are further improved. In addition, the model solves the parameters of the variation function of the ordinary Kriging interpolation and reduces the generation error of the formation data interpolation. The results of relevant experiments show that the hybrid sparrow optimization Kriging model improves the accuracy and convergence speed compared with other swarm intelligence algorithms and that the accuracy of this model is improved by 8.4% compared with the original Kriging interpolation algorithm. Based on the hybrid sparrow optimization Kriging model, we propose a three-dimensional stratigraphic model for the Yangchangwan Coal Mine, which provides further support for mining operations and three-dimensional stratigraphic research in this area. The accuracy and applicability of the hybrid sparrow optimization Kriging model are further explained using a case study with the stratigraphic model data in the Yangchangwan Coal Mine. HSSA with significant potential for applications in industries such as coal mining and geological exploration. In these fields, the efficient acquisition, processing, and modeling of stratigraphic data are critical for enhancing geological interpretation and optimizing operational workflows.

Analysis of Landscape Architecture Planning and Design Under the Background of Multimedia Based on Swarm Intelligence Optimization Path Algorithm

Analysis of Landscape Architecture Planning and Design Under the Background of Multimedia Based on Swarm Intelligence Optimization Path Algorithm

Study on bionics-based swarm intelligence optimization algorithms for wavelength selection in near-infrared spectroscopy

Wavelength selection is one of the most important steps in the modeling of near-infrared spectroscopy (NIRS), which is of great significance to reduce model complexity and improve model performance. In this paper, a total of ten bionics-based swarm intelligence optimization algorithms (BSIOAs) inspired by natural creatures, such as Harris Hawks Optimization (HHO), Butterfly Optimization Algorithm (BOA), Whale Optimization Algorithm (WOA), Monarch Butterfly Optimization (MBO), Grey Wolf Optimization (GWO), Fruit Fly Optimization Algorithm (FOA), Bat Algorithm (BA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) were studied on application to wavelength selection in the NIRS modeling. Three benchmark NIRS datasets were used to evaluate the algorithms by calculating the indicators, including coefficients of determination, root mean square error, and residual predictive deviation in calibration and prediction. The results obtained showed that these BSIOAs can significantly reduce the number of wavelengths (retaining half or fewer). Compared with the full-spectrum models, the present models not only simplified the model structures but improved the model performances. The performances were generally better than the ones by some popular and classic wavelength selection algorithms, such as competitive adaptive reweighted sampling, Monte Carlo uninformative variable elimination, variable importance in projection, interval partial least-squares, and successive projections algorithm.

A novel memory usage guided fuzzing based on particle swarm optimization

Fuzzing has become the focus of research in the field of software testing due to its advantages such as simple testing process, easy deployment, and easy reproduction of crashes. However, fuzzing also suffers from the disadvantages of poor test purpose and invalid generated seeds. To solve the above problems, researchers proposed the Memory Usage Guided Fuzzing (MUGF). To further optimize the performance of MUGF, this paper proposes a fuzzing method named Memory Usage Guided Fuzzing based on Particle Swarm Optimization (MUGF-PSO). MUGF-PSO will guide the selection of mutation operators for subsequent testing by learning the effectiveness of each mutation operators during previous testing. Specifically, MUGF-PSO regards each mutation operator as a particle in the particle swarm optimization algorithm, and the coverage change is regarded as an important factor to evaluate the seed’s local and global best position. In addition, the efficient selection probability distribution of mutation operator is constantly iteratively searched to make the MUGF tends to select the mutation operator that is more able to trigger new coverage. Furthermore, the MUGF-PSO is integrated into MemLock developed by MUGF, that is, MemLock-PSO is developed. We conduct a comparison experiment with 5 fuzzers (including AFL, MemLock, EcoFuzz, HavocMAB and Darwin), and the results show that MemLock-PSO is able to find more paths and crashes in 12 widely used program with different functions from 10 tools, with a significant gap of around 12 h. Meanwhile, the MemLock-PSO have a significant difference in the number of paths and crashes compared to other fuzzers with a good stability. Our work also proves the remarkable significance of applying swarm intelligence optimization algorithms in fuzzing in order to solve the problem of selection of mutation operators.

Swarm intelligent optimization algorithms for precision gene selection in microarray-based cancer classification

Swarm intelligent optimization algorithms for precision gene selection in microarray-based cancer classification

Rolling bearing fault diagnosis based on optimized A-BiLSTM

In order to improve the efficiency of hyperparameter setting and its adaptability to the model, and to reduce the high cost and low efficiency of manually setting model parameters, a rolling bearing fault diagnosis method based on Honey Badger Algorithm (HBA) optimized attention bidirectional long short-term memory network (HBA-A-BiLSTM) is proposed. Firstly, the optimal hyperparameter combination of A-BiLSTM model is searched by HBA, and then the fault diagnosis performance of A-BiLSTM model under the optimal hyperparameter is tested. Finally, the generalization ability of the model is tested based on data sets under different working conditions. The fault diagnosis effect of the proposed method is verified by using CWRU data set, and the diagnosis accuracy and confusion matrix are used for evaluation. The experimental results show that compared with other swarm intelligence optimization algorithms, the Honey Badger algorithm has good global search performance and fast convergence speed. The fault diagnosis accuracy of the optimized final model reaches 99.5%, which has good effects. It can also achieve stable and accurate fault diagnosis performance under different working conditions and has strong generalization ability.

Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application.

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective optimization issues in recent years. Their study has garnered a lot of attention since multi-objective optimization problems have a hard high-dimensional goal space. The black-winged kite optimization algorithm still suffers from the imbalance between global search and local development capabilities, and it is prone to local optimization even though it combines Cauchy mutation to enhance the algorithm's optimization ability. The heuristic optimization algorithm of the black-winged kite fused with osprey (OCBKA), which initializes the population by logistic chaotic mapping and fuses the osprey optimization algorithm to improve the search performance of the algorithm, is proposed as a means of enhancing the search ability of the black-winged kite algorithm (BKA). By using numerical comparisons between the CEC2005 and CEC2021 benchmark functions, along with other swarm intelligence optimization methods and the solutions to three engineering optimization problems, the upgraded strategy's efficacy is confirmed. Based on numerical experiment findings, the revised OCBKA is very competitive because it can handle complicated engineering optimization problems with a high convergence accuracy and quick convergence time when compared to other comparable algorithms.

Hybrid Multi-Objective Chameleon Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications.

Aiming at the problems of chameleon swarm algorithm (CSA), such as slow convergence speed, poor robustness, and ease of falling into the local optimum, a multi-strategy improved chameleon optimization algorithm (ICSA) is herein proposed. Firstly, logistic mapping was introduced to initialize the chameleon population to improve the diversity of the initial population. Secondly, in the prey-search stage, the sub-population spiral search strategy was introduced to improve the global search ability and optimization accuracy of the algorithm. Then, considering the blindness of chameleon's eye turning to find prey, the Lévy flight strategy with cosine adaptive weight was combined with greed strategy to enhance the guidance of random exploration in the eyes' rotation stage. Finally, a nonlinear varying weight was introduced to update the chameleon position in the prey-capture stage, and the refraction reverse-learning strategy was used to improve the population activity in the later stage so as to improve the ability of the algorithm to jump out of the local optimum. Eighteen functions in the CEC2005 benchmark test set were selected as an experimental test set, and the performance of ICSA was tested and compared with five other swarm intelligent optimization algorithms. The analysis of the experimental results of 30 independent runs showed that ICSA has stronger convergence performance and optimization ability. Finally, ICSA was applied to the UAV path-planning problem. The simulation results showed that compared with other algorithms, the paths generated by ICSA in different terrain scenarios are shorter and more stable.

An ensemble optimizer with a stacking ensemble surrogate model for identification of groundwater contamination source

The application of the simulation-optimization method for groundwater contamination source identification (GCSI) encounters two main challenges: the substantial time cost of calling the simulation model, and the limitations on the accuracy of identification results due to the complexity, nonlinearity, and ill-posed nature of the inverse problem. To address these issues, we have innovatively developed an inversion framework based on ensemble learning strategies. This framework comprises a stacking ensemble model (SEM), which integrates three distinct machine learning models (Extremely Randomized Trees, Adaptive Boosting, and Bidirectional Gated Recurrent Unit), and an ensemble optimizer (E-GKSEEFO), which combines two newly proposed swarm intelligence optimizers (Genghis Khan Shark Optimizer and Electric Eel Foraging Optimizer). Specifically, the SEM serves as a surrogate model for the groundwater numerical simulation model. Compared to the original simulation model, it significantly reduces time cost while maintaining accuracy. The E-GKSEEFO, functioning as the search strategy for the optimization model, greatly enhances the accuracy of the optimization results. We have verified the performance of the SEM-E-GKSEEFO ensemble inversion framework through two hypothetical scenarios derived from an actual coal gangue pile. The results are as follows. (1) The SEM exhibits improved fitting performance compared to single machine learning models when dealing with high-dimensional nonlinear data from GCSI. (2) The E-GKSEEFO achieves significantly higher accuracy in the identification results of GCSI than individual optimizers. These findings affirm the effectiveness and superiority of the proposed SEM-E-GKSEEFO ensemble inversion framework.