Colliding Bodies Optimization Research Articles

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.

Special issue on International Conference on Computing and Communication Networks

Special issue on International Conference on Computing and Communication Networks

Application of a weighted graph representation method to the crashworthiness optimization of subway collision frame structures

The collision frame structure is the most important subway structure for energy absorption and impact load transfer during foreign object collision. To improve the crashworthiness of the collision frame structure, a parameter optimization method based on a weighted graph representation method was proposed. Based on weighted graph theory, an equivalent mathematical model of the collision frame structure was established. Combined with the Belytschko beam element method, which is a large deformation calculation technique, a parametric finite element model (FEM) considering plastic mechanical properties was constructed, which was validated by a detailed finite element model and a quasi-static crush test. Based on the practical requirements, the crush displacement, the intrusion space and the mean crush force (MCF) were determined as constraints. The structural total mass was considered as the objective. The optimized structural parameters and key performance indicators were obtained and compared by the genetic algorithm (GA), Colliding bodies optimization (CBO), Enhanced colliding bodies optimization (ECBO). The results showed that the GA algorithm had the best optimization objective. The comparison results showed that the optimized collision frame structure could meet the crashworthiness requirements, while the structural mass was reduced from the initial 168.92 kg to 97.33 kg, thus giving a reduction rate of 42.38 %.

Intelligent metaheuristics with optimal machine learning approach for malware detection on IoT‐enabled maritime transportation systems

AbstractThe latest advancements in Internet of Things (IoT) have revolutionized the productivity of global shipping industry in the recent years. It also led to the emergence of IoT‐enabled Maritime Transportation Systems (MTS). These approaches detect the malware in network before the execution process. Various machine learning (ML) models have been proposed and designed in literature for effective malware detection. However, the existence of numerous features in the data bring dimensionality problem which can be only resolved by the use of feature selection approaches. Therefore, the current research work presents Intelligent Metaheuristics‐based Feature Selection model with Optimal ML approach for Malware Detection (IMFSOML‐MD) on IoT‐enabled MTS. Primarily, IMFSOML‐MD technique involves the design of Quantum Invasive Weed Optimization Algorithm‐based Feature Selection technique to optimally choose a subset of features. Moreover, an Optimal Wavelet Neural Network (OWNN) model is employed to perform classification process. The initial parameters of WNN model are optimally tuned with the help of Colliding Bodies Optimization algorithm thereby improving the detection performance. The proposed IMFSOML‐MD technique was experimentally validated using publicly‐available CICMalDroid2020 dataset. The results from extensive comparative analysis demonstrated the superiority of the proposed IMFSOML‐MD technique over other compared methods in terms of detection performance with maximum accuracy of 98.96%.

Grid-connected solar photovoltaic-fed brushless DC motor drive for water pumping system using colliding body optimization technique

PurposeThis paper aims to present an improvement to the power quality of the grid by using a colliding body optimization (CBO) based proportional-integral (PI) compensated design for a grid-connected solar photovoltaic-fed brushless DC motor (BLDC)-driven water pumping system with a bidirectional power flow control. The system with bidirectional power flow allows driving the pump at full proportions uninterruptedly irrespective of the weather conditions and feeding a grid when water pumping is not required.Design/methodology/approachHere, power quality issue is taken care of by the optimal generation of the duty cycle of the voltage source converter. The duty cycle is optimally generated by optimal selection of the gains of the current controller (i.e. PI), with the CBO technique resulting in a nearly unity power factor as well as lower total harmonic distortion (THD) of input current. In the CBO technique, the gains of the PI controller are considered as agents and collide with each other to obtain the best value. The system is simulated using MATLAB/Simulink and validated in real time with OPAL RT simulator, OP5700.FindingsIt was found that the power quality of grid using the CBO technique has improved much better than the particle swarm optimization and Zeigler–Nichols approach. The bidirectional flow of control of VSC allowed for optimum resource utilization and full capacity of water pumping whatever may be weather conditions.Originality/valueImproved power quality of grid by optimally generation of the duty cycle for the proposed system. A unit vector tamplate generation technique is used for bidirectional power transfer.

A Framework for Optimization of Double Layer Grids with Supporting Cables

Space structures are used when it is required to cover a large unpaved area such as sports stadiums, aircraft factories, bridges, etc. Weight minimization is important in optimizing space structures, having various advantages such as cost reduction, reduction of the deterioration, increasing the possibility of using larger spans, and easy transportation and installation process. The use of meta-heuristic algorithms, inspired by the physics laws of nature, allows us to optimize a variety of complex problems. In this paper, four structures with different spans with and without cables are examined. Optimization is performed using Enhanced Colliding Bodies Optimization (ECBO) and Vibrating Particles System (VPS) optimization algorithms. The optimization is aimed to minimize the weight and the mid-span deflection of the structures. The utilized methods are based on the optimization algorithms in MATLAB (MATrix LABoratory) programming and the SAP2000 (Structural Analysis Program 2000) finite element software. In this way, the results, obtained from the structural analysis in finite element software, are utilized by the optimization algorithms and the operation is repeated until the optimal weight is achieved. In all the investigated examples, the ECBO performed better than the VPS algorithm. The findings indicate that the addition of the cables, in addition of reducing the weight of the structures can reduce the mid-span deflection.