Colliding Bodies Optimization Research Articles

Optimal Keyword Selection by Hybrid Optimization with Itemset Mining for Text Summarization in Biomedical Sector

Biomedical literature is growing exponentially, creating challenges for researchers and clinicians to access relevant information efficiently. Automatic biomedical text summarization is a promising solution to this issue, allowing the extraction of essential information while reducing redundancy. Traditional summarization techniques often rely on shallow linguistic features or simple term frequencies, which fail to capture the complex relationships in biomedical texts. This paper explores advanced methods for biomedical text summarization, including graph-based approaches, frequent item set mining, and deep learning models such as BERT. The proposed framework introduces a hybrid optimization technique combining Colliding Bodies Optimization (CBO) and Cuckoo Search Optimization (CSO) for optimal keyword selection, alongside a Recurrent Neural Network (RNN) for sentence categorization. Extensive experimentation using UMLS-based concept extraction, keyword selection, and Apriori-based itemset mining demonstrates that the proposed method significantly outperforms existing models in both the in formativeness and coherence of generated summaries. The results reveal that combining deep language models with domain-specific knowledge enhances summarization quality and can be effectively applied to diverse types of biomedical text.

An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission.

With its increasing global prevalence, lung cancer remains a critical health concern. Despite the advancement of screening programs, patient selection and risk stratification pose significant challenges. This study addresses the pressing need for early detection through a novel diagnostic approach that leverages innovative image processing techniques. The urgency of early lung cancer detection is emphasized by its alarming growth worldwide. While computed tomography (CT) surpasses traditional X-ray methods, a comprehensive diagnosis requires a combination of imaging techniques. This research introduces an advanced diagnostic tool implemented through image processing methodologies. The methodology commences with histogram equalization, a crucial step in artifact removal from CT images sourced from a medical database. Accurate lung CT image segmentation, which is vital for cancer diagnosis, follows. The Otsu thresholding method and optimization, employing Colliding Bodies Optimization (CBO), enhance the precision of the segmentation process. A local binary pattern (LBP) is deployed for feature extraction, enabling the identification of nodule sizes and precise locations. The resulting image underwent classification using the densely connected CNN (DenseNet) deep learning algorithm, which effectively distinguished between benign and malignant tumors. The proposed CBO+DenseNet CNN exhibits remarkable performance improvements over traditional methods. Notable enhancements in accuracy (98.17%), specificity (97.32%), precision (97.46%), and recall (97.89%) are observed, as evidenced by the results from the fractional randomized voting model (FRVM). These findings highlight the potential of the proposed model as an advanced diagnostic tool. Its improved metrics promise heightened accuracy in tumor classification and localization. The proposed model uniquely combines Colliding Bodies Optimization (CBO) with DenseNet CNN, enhancing segmentation and classification accuracy for lung cancer detection, setting it apart from traditional methods with superior performance metrics.

An efficient decoder for size optimization of trusses with automatic member grouping

This paper presents an efficient decoding method (namely SORTED decoding) for the de facto encoding in size optimization of trusses with automatic member grouping. The efficiency of the proposed method is evaluated through numerical experiments driven by two metaheuristic algorithms of different search mechanisms: Colliding Bodies Optimization and Jaya Algorithm. The first group of experiments show that the SORTED decoding method significantly outperforms the de facto and the two other decoding methods introduced in this study (ACCUMULATED and SORTED-ACCUMULATED) in terms of solution quality; moreover, the authors proved that the superiority of the SORTED decoding is not algorithm-dependent. Considerably better member-grouping configurations that provide up to 15% material economy are discovered for some of the well-known pre-grouped benchmark problems in the second group of experiments; surprisingly, the worst member-grouping configuration discovered by the SORTED decoding for one of the problems is better than that of the pre-grouped version.

Research on the performance of GMCBO methodology based on model updating of a pedestrian bridge

Consider the loss of diversity of the bodies at the later optimization stage and the low efficiency of the global searching ability of the traditional colliding bodies optimization algorithm in dealing with complex optimization issues of practical structures. Herein, an improved colliding bodies optimization method combing Gaussian-white-noise mutation and innovative collision mode is proposed. The introduction of Gaussian-white-noise is mutating the positions of the selected bodies and enriching the diversity during the whole optimization process. The changed collision mode from ‘one to one’ to ‘one to best’ is accelerating the process to global extremum. During searching the global extremums of the 12 test functions which are unimodal, multimodal and multimodal with fix dimension, the proposed shows relatively better accuracy and efficiency. The comparison based on the maximum, minimum, average, standard deviation and non-parametric Wilcoxon test of the optimization results by the above algorithms confirmed the outstanding performance of the proposed method. Through the application of the proposed method to the model updating of a pedestrian bridge, all the differences of the first five frequencies between the measured vibration data and finite element model are reduced from approximate 25% to nearly 5%, and the updated dynamic characteristics show great agreement with the practical structure. From the comparison of the responses between updated model and human-induced vibration experiment, most of the response differences are around 10%. The application of the above real structure further verifies that the proposed method is feasible and strongly recommended in optimization issues of practical structures.

Damage detection of truss structures using meta-heuristic algorithms and optimized group method of data handling surrogate model

Optimization-based methods are increasingly being implemented for structural damage detection (SDD) problems through the minimization of the objective functions based on vibration data. Meanwhile, the challenge of high computational cost hinders the applied use of SDD methods, especially upon addressing large-scale structures. In this study, a two-stage damage detection approach is proposed for damage localization as well as extent quantification which reduces the computational time of SDD problems. In the first stage of the proposed framework, suspected locations of damage are identified by employing a residual force vector. Then in the second stage, damage extent of already identified elements will be assessed by model updating method through three different optimization algorithms, namely particle swarm optimization (PSO), differential evolution (DE), and enhanced colliding bodies optimization (ECBO). To spare time-consuming modal analysis in each iteration of optimization algorithms, a PSO-based group method of data handling (GMDH) polynomial neural network (PNN) surrogate model is proposed. Performance of GMDH PNN strongly depends on the input variables, the number of input variables, and the order of the polynomial. The PSO-based GMDH PNN, developed in this study, employs PSO for the optimum selection of these parameters. Furthermore, the efficiency of Elemental Energy Quotient Indicator (EEQI) as a new damage index proposed in this paper is compared with the approach based on the natural frequency-based index. This paper also deals with the incomplete modal data measured from limited number of sensors. This problem is addressed by employing the model reduction technique and solving the SDD problem with modal information obtained from the reduced model. Numerical examples show that the suggested PSO-based GMDH surrogate model is an accurate tool for various types of SDD problems, and it can provide promising results with a significant reduction in computational time.

Optimal design of offshore jacket platform using enhanced colliding bodies optimization algorithm

Given the considerable volume of materials used in jacket platforms, structural optimization of these structures is always of interest. In the design optimization of offshore jacket platforms, the objective function is iteratively evaluated through a number of complex and time-consuming analyses making the optimization process computationally expensive. To reduce the computational costs, therefore, it is imperative to investigate efficient optimization algorithms with a high convergence rate to achieve optimal solutions for offshore jacket structures as a large-scale and complex problem. Accordingly, this research studies the application of a novel metaheuristic algorithm called Enhanced Colliding Bodies Optimization (ECBO) for the design optimization of a real jacket platform, SPD19A. The optimization constraints comprise stress and buckling in the members, horizontal displacements at the working point, and structural adequacy control of connections. The optimization results are subsequently compared to a design optimized by the Genetic Algorithm (GA), as an example, to evaluate the efficiency of the ECBO algorithm for the offshore jacket structure. The outcomes indicate that ECBO optimizes the jacket more effectively by 15%, while the optimization ratio of GA is 11%. Hence, the results confirm that ECBO has great and favorable efficiency and can potently escape from local optima to reach a better design for the jacket structure.

Optimal design of truss domes with frequency constraints using seven metaheuristic algorithms incorporating a comprehensive statistical assessment

In this article, seven recently developed metaheuristic algorithms are utilized for optimal design of dome-shaped trusses with natural frequency constraints. These algorithms are social network search, jellyfish search optimizer, equilibrium optimizer, teaching-learning-based optimization, grey wolf optimizer, colliding bodies optimization, and the improved grey wolf optimizer. This study focuses on the assessment and tuning of these algorithms to enhance both computational efficiency and solution quality. In particular, we introduce a hyperparameter tuning procedure that significantly improves the performance of the algorithms to make them more suitable for the solution of structural optimization problems. The performance of the selected algorithms is studied through five benchmark examples for design optimization of dome structures, from small-scale to large-scale cases, with frequency constraints. Many combination scenarios are studied for hyperparameter tuning of the algorithms in each design example in order to provide comprehensive statistical results determining the sufficient number of structural analyses as well as the number of particles to find an efficient condition of each algorithm. Indeed, this study also presents an insight into the efficient setting of hyperparameters for each algorithm, leading to a better solution with lower computational cost. Furthermore, the optimization results provide a comparison of the solutions obtained in the current and previous studies in order to demonstrate the suitability of the selected algorithms.

Multi-objective Colliding Bodies Optimization Algorithm for the Obnoxious p-median Problems

The obnoxious p-median problem consists of locating p facilities among a set of sites such that the sum distance from any demand to its nearest facility and the dispersion among facilities are maximized. In this paper, the multi-objective colliding bodies optimization algorithm (MOCBO) is utilized to obtain the trade-off curve of the obnoxious p-median problems. The performance of the developed optimization method is investigated for locating obnoxious facilities through two case studies to maximize the two conflicting objectives. The performance of the MOCBO algorithm is further compared with those of the MPSO and NSGA-II algorithms representative of the state of the art in the field of multi-objective optimization. In this study, the MOCBO algorithm showed suitable convergence performance and generalization abilities compared to the MPSO and NSGA-II algorithms.

Optimal design of self-centering bi-rocking braced frames using metaheuristic algorithms

Residual drift of structural systems after seismic events can make buildings uninhabitable and may cause their collapse in aftershocks, as seen in the 2023 Turkey-Syria earthquake. Self-centering rocking-core systems can eliminate plastic deformation of structures after earthquakes. In the current study, a bi-rocking steel-braced frame was designed using the modified modal superposition (MMS) method. The optimal location of the secondary joint was found to minimize the base shear, overturning moment, peak floor acceleration and inter-story drift. Damage to non-structural components has been considered. The models included 12-, 18- and 24- story frames that were subjected to far-field (FF), near-field-pulse (NP) and near-field-no-pulse (N) ground motion records. The seismic records were scaled to the maximum credible earthquake level. Nonlinear time-history analyses were performed using OpenSees open-source finite element software. Additionally, post-tensioned cables and energy dissipation fuses as variable parameters were optimized using particle swarm optimization. The performance of seven metaheuristic algorithms was compared and the results showed that placing the secondary joint at the mid-height of the structure reduced all seismic demand. The results also suggest that the story moment and FF records were most sensitive to the location of the secondary joint. It was observed that frames with lower stiffness and yield strength of the damper exhibited better seismic performance. Based on this, corrective percentages have been proposed for the design of the cable and dampers. A self-centering ratio of 1.07 was suggested at the mid-height and 1.19 for the base damper. The colliding bodies optimization algorithm performed best for time-history analysis-based optimization.

Adaptive membership enhanced fuzzy classifier with modified LSTM for automated rainfall prediction model

Nowadays, various research works is explored to predict the rainfall in the different areas. The emerging research is assisted to make effective decision capacities that are involved in the field of agriculture broadly related to the irrigation process and cultivation. Here, the atmospheric and climatic factors such as wind speed, temperature, and humidity get varies from one place to another place. Thus, it makes the system more complex, and it attains higher error rate during computation for providing accurate rainfall prediction results. In this paper, the major intention is to design an advanced Artificial Intelligent (AI) model for rainfall prediction for different areas. The rainfall data from diverse areas are collected initially, and data cleaning is performed. Further, data normalization is done for ensuring the proper organization and related data in each record. Once these pre-processing phases are completed, rainfall recognition is the main step, in which Adaptive Membership Enhanced Fuzzy Classifier (AME-FC) is adopted for classifying the data into low, medium, and high rainfall. Then for each degree of low, medium, and high rainfall, the prediction process is performed individually by training the developed Tri-Long Short-Term Memory (TRI-LSTM). Additionally, the output achieved from the trained TRI-LSTM rainfall prediction in cm for each low, medium, and high rainfall. The meta-heuristic technique with Hybrid Moth-Flame Colliding Bodies Optimization (HMFCBO) enhances the recognition and prediction phases. The experimental outcome shows that the different rainfall prediction databases prove the developed model overwhelms the conventional models, and thus it would be helpful to predict more accurate rainfall.

Reliability-based design optimization of post-tensioned self-centering rocking steel frame structures

This paper reports the Reliability-Based Design Optimization (RBDO) of post-tensioned self-centering rocking steel frame structures with buckling-restrained bracing systems. The objective was to investigate the reliability index, optimization of self-centering structures to reach minimum weight using metaheuristic algorithms, and their application in linear and nonlinear reliability problems. For this purpose, three steel structures (10-, 15-, and 20-storey structures) were each evaluated with three steel framing systems (buckling-restrained bracing, post-tensioned self-centering multiple rocking steel frame with buckling-restrained bracing, and post-tensioned self-centering single rocking steel frame with buckling-restrained bracing), making a total of nine models. Subsequently, specific 2D frames of the structural models were numerically simulated using nonlinear dynamic analysis (NDA) in OpenSees software. Optimization was carried out in two stages: with and without considering the uncertainties. Finally, for calculating the failure probability and structural reliability index, Monte-Carlo Simulation (MCS) was employed. Results indicate that, although considering the reliability index leads to heavier structures, structural safety was increased and failure probability was reduced to even lower specified levels. Furthermore, among the incorporated algorithms, the Colliding Bodies Optimization (CBO) and Charged System Search (CSS) algorithms proved to have superior performances in terms of accuracy and converging time. On average, using these two algorithms for cases without uncertainty resulted in maximum weight reductions of 36%, 30%, and 32% for 10-, 15-, and 20-storey structures. In addition, for cases with uncertainty, these two algorithms with reliability index (β) of 2 respectively reduced the weight of 10-, 15-, and 20-storey structures by 21%. Also, it was shown that not considering the uncertainties can increase the failure probability by up to 23%.

Simultaneous estimation of thermal and optical properties of molten salt based on improved colliding bodies optimization

Inverse estimation of thermal and optical properties of molten salt is studied in this study. An improved colliding bodies optimization (ICBO) algorithm is developed to solve inverse problems, in which three improvements is proposed: (1) chaos theory is introduced to optimize initial positions of colliding bodies; (2) a self-memory strategy is proposed to improve local search ability; and (3) a random search strategy is proposed to improve global search ability. And the test functions prove that the ICBO algorithm is more efficient and robust than the original colliding bodies optimization algorithm. A two-step retrieval strategy is proposed to overcome serious crosstalk problem among different parameters, in which optical parameters are inversed based on the transmittance-reflectance model firstly, and then thermal parameters are inversed based on the transient coupled conduction-radiation model and known optical parameters obtained in the first step. All research outcomes indicate that the constant and temperature-dependent thermal and optical parameters of molten salt can be estimated accurately by the ICBO algorithm and two-step retrieval strategy. Even if considering 10% of measurement error, the maximum relative error is only 6.50% (estimation of thermal conductivity). Therefore, this work is beneficial for improving utilization of molten salt for harvesting solar energy.

Research on Vibration Control of Landscape Tower-TMDI Based on Colliding Bodies Optimization

Research on Vibration Control of Landscape Tower-TMDI Based on Colliding Bodies Optimization

Buckling resistance prediction of high-strength steel columns using Metaheuristic-trained Artificial Neural Networks

The buckling behavior of columns, as the most influential members regarding the stability of structures, has been a long-standing field of interest. Moreover, due to the conservative attitude of classical theories, the ultimate buckling load obtained through these recommendations is mainly considered an underestimation of its actual value. This paper hence aims to develop practical metaheuristic-trained Artificial Neural Networks to predict the ultimate buckling load of High Strength Steel columns. Therefore, initially, the ABAQUS Finite Element Model was verified with experimental results and showed 99.82% accuracy; then, nonlinear finite element analyses for 114 Models were conducted to be utilized as datasets to develop ANNs. To do so, the section height, flange width, web thickness, flange thickness, and steel yield strength were considered the input variables, while the ultimate buckling load was assumed to be the only output variable. ANN models are typically trained through algorithms such as LM, BR, and SCG; meanwhile, the novelty of this work is to take advantage of metaheuristics to optimize weights and biases of ANNs. In this regard, four metaheuristic algorithms, namely Particle Swarm Optimization, Colliding Body Optimization, and their developed version by applying Genetic Algorithm, Particle Swarm Optimization–Genetic Algorithm, and Colliding Body Optimization–Genetic Algorithm, have been used to train ANNs efficiently. Besides, their performances were evaluated using statistical relations like Coefficient of Correlation and Mean Square Error. The results illustrate that the trained models could accurately predict the ultimate buckling loads up to 99.8%, demonstrating the efficiency and precision of the present work.

Optimum Design of Cable Domes Using Enhanced Colliding Bodies Optimization Algorithm with the Substructuring Method

Optimum Design of Cable Domes Using Enhanced Colliding Bodies Optimization Algorithm with the Substructuring Method

Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength

In recent years, artificial neural network (ANN) is one of the popular and effective machine learning models that can be used to accurately predict fiber reinforced polymer (FRP) strength. However, the ANN structure and parameters are usually are chosen by experience. In this study, the aim is to use the combination of meta-heuristic algorithm with two different types of artificial neural network structure to optimize the parameters of the feed forward backpropagation and radial basis function networks. In this paper, Particle swarm optimization (PSO), Genetic algorithm (GA), Colliding bodies optimization (CBO), Enhanced colliding bodies optimization (ECBO) algorithms are used to combine with ANNs.A total of 223 test data on Carbon FRP (CFRP) collected from the available literature were used to generate training and test data sets. Various validation criteria such as mean square error, root mean square error and correlation coefficient (R) are used to validate the models. These models consider the effects of concrete compressive strength, concrete sample diameter, concrete sample length, fiber elastic modulus, fiber thickness, fiber strength on the ultimate strength of FRP-concrete.It is shown that ECBO algorithm provide better results and higher accuracy. Also, the comparison of the results of using two neural networks, feed forward backpropagation (FFB) and radial basis function (RBF), indicates that the lower error percentage and more accurate results are related to the FFB neural networks combined with ECBO.

Colliding Bodies Optimization With Deep Belief Network Based Robust Pedestrian Detection

Colliding Bodies Optimization With Deep Belief Network Based Robust Pedestrian Detection

Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past data to detect and classify PD. With this motivation, the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine (CBO-OKELM) for diagnosis and classification of PD. The goal of the proposed CBO-OKELM technique is to identify whether PD exists or not. CBO-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection (CBO-FS) technique for optimal subset of features. In addition, Water Strider Algorithm (WSA) with Kernel Extreme Learning Machine (KELM) model is also developed for the classification of PD. CBO algorithm is used to elect the optimal set of features whereas WSA is utilized for parameter tuning of KELM model which altogether helps in accomplishing the maximum PD diagnostic performance. The experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%, 96.34%, 92.49%, and 92.36% on Speech PD, Voice PD, Hand PD Meander, and Hand PD Spiral datasets respectively.

Research on Vibration Control of Power Transmission Lines-TMDI Based on Colliding Bodies Optimization

To investigate the vibration control capability of a tuned mass damper inerter (TMDI) on a transmission line, the motion equations of the transmission line with TMDI under harmonic excitation were derived. Thus, the closed-form solutions of the displacement response spectrum were obtained by Fourier transform. Based on the colliding bodies optimization (CBO), one of the metaheuristic algorithms, the TMDI parameters, was optimized to minimize the displacement of the transmission line-TMDI system. The research results show that the response of the transmission line was reduced by at least half for different mass ratio and frequency ratio conditions, which indicates that the TMDI can effectively control the displacement response of the transmission line. In addition, the TMDI parameters were optimized by CBO, and the vibration control efficiency was significantly improved. The results of the study show that the data converge quickly with fewer iterations in collision body optimization. On the one hand, CBO avoids getting into local optimization compared to other metaheuristic algorithms. On the other hand, it is cheaper in terms of the cost of its calculations compared to the methods of mathematical derivation. It plays an active role in the optimization of complex structures. The vibration suppression performance of the TMDI after optimization reaches 56–96%.

Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm

Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be adjusted with many existing algorithms, making them difficult to use. In real-world problems, many of these algorithms must be combined or hybridized, which results in more complex and time-consuming algorithms. This paper presents a new artificial parameter-less optimization algorithm (APLO) for parameter estimation of PV models. New mutation operators are designed in the proposed algorithm. APLO’s exploitation phase is enhanced by each individual searching for the best solution in this updating operator. Moreover, the current best, the old best, and the individual’s current position are utilized in the differential term of the mutation operator to assist the exploration phase and control the convergence speed. The algorithm uses a random step length based on a normal distribution to ensure population diversity. We present the results of a comparative study using APLO and well-known existing parameter-less meta-heuristic algorithms such as grey wolf optimization, the salp swarm algorithm, JAYA, teaching-learning based optimization, colliding body optimization, as well as three major parameter-based algorithms such as differential evolution, genetic algorithm, and particle swarm optimization to estimate the parameters of PV the modules. The results revealed that the proposed algorithm could provide excellent exploration–exploitation balance and consistency during the iterations. Furthermore, the APLO algorithm shows high reliability and accuracy in identifying the parameters of PV cell models.