Enhanced Colliding Bodies Optimization Research Articles

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.

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.

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 %.

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.

Optimized hybrid ensemble technique for CMIP6 wind data projections under different climate-change scenarios. Case study: United Kingdom

Wind energy resources will be impacted by climate change. A novel hybrid ensemble technique is presented to improve long-term wind speed projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) data from global climate models. The technique constructs an optimized system, which relies on a Genetic Algorithm and an Enhanced Colliding Bodies Optimization technique. Next, the performance of the proposed method over a target area (United Kingdom) is evaluated between 1950 and 2014. Finally, to avoid single-valued deterministic projections and mitigate the uncertainties, the improved wind speed data series are investigated considering different climate-change scenarios – the Shared Socioeconomic Pathways (SSPs) – for the period 2015–2050. The performance of different CMIP6 models is found to differ over time and space. In the target area the data derived from the Hybrid model confirm that extreme wind events will occur more frequently. The monthly mean wind speed is expected to increase from 3.41 m/s during 1950–2014 to 3.60, 3.63, 3.48, 3.59 and 3.61 m/s during the study period in the SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0 and SSP5-8.5 climate-change scenarios, respectively. More generally, the results prove that the Hybrid model is highly effective in improving the accuracy, direction and geographical patterns of the data, and this novel method can narrow the potential uncertainties of numerical simulations.

Optimization of Columns and Bent Caps of RC Bridges for Cost and CO2 Emission

This paper describes a methodology for optimal seismic design of reinforced concrete 3D columns and bent caps (beams) of bridges. Design variables include compressive strength of concrete, geometry, as well as longitudinal and shear reinforcement of columns and beams. The optimization is performed to minimize the cost and CO2 emissions using the enhanced colliding bodies optimization (ECBO) algorithm. The trade-off between cost and CO2 emissions shows that in the design for minimizing CO2 emissions compared to the design based on the cost minimization, increasing 1.4 % in cost can decrease CO2 emissions by 6.1%.

Discrete Optimum Design of Planar Steel Curved Roof and Pitched Roof Portal Frames Using Metaheuristic Algorithms

Portal frames are single-story frame buildings including columns and rafters, and their rafters can be either curved or pitched. These are used widely in the construction of industrial buildings, warehouses, gyms, fire stations, agricultural buildings, hangars, etc. The construction cost of these frames considerably depends on their weight. In the present research, the discrete optimum design of two types of portal frames including planar steel Curved Roof Frame (CRF) and Pitched Roof Frame (PRF) with tapered I-section members are presented. The optimal design aims to minimize the weight of these frame structures while satisfying some design constraints based on the requirements of ANSI/AISC 360-16 and ASCE 7-10. Four population-based metaheuristic optimization algorithms are applied to the optimal design of these frames. These algorithms consist of Teaching-Learning-Based Optimization (TLBO), Enhanced Colliding Bodies Optimization (ECBO), Shuffled Shepherd Optimization Algorithm (SSOA), and Water Strider Algorithm (WSA). Two main objectives are followed in this paper. The first one deals with comparing the optimized weight of the CRF and PRF structures with the same dimensions for height and span in two different span lengths (16.0 m and 32.0 m), and the second one is related to comparing the performance of the considered metaheuristics in the optimum design of these portal frames. The obtained results reveal that CRF is more economical than PRF in the fair comparison. Moreover, comparing the results acquired by SSOA with those of other considered metaheuristics reveals that SSOA has better performance for the optimal design of these portal frames.

A Comparative Study of the Optimum Tuned Mass Damper for High-rise Structures Considering Soil-structure Interaction

The present paper focuses on the optimum design of tuned mass damper (TMD) as a device for control of the structures. The optimum free vibration parameters such as period and damping ratio depend on the soil condition. For this reason, the seven meta-heuristic algorithms namely colliding bodies optimization (CBO), enhanced colliding bodies optimization (ECBO), water strider algorithm (WSA), dynamic water strider algorithm (DWSA), ray optimization (RO) algorithm, teaching-learning-based optimization (TLBO) algorithm and plasma generation optimization (PGO) are used to find the TMD parameters considering soil-structure interaction (SSI) effects. These optimization methods are applied to a benchmark 40-story structure. For comparison, the obtained results of these algorithms are compared. The capability and robustness of the algorithms are investigated through the benchmark problem. The results are shown that the soil type affects the optimum values of the TMD parameters, especially for the soft soil. To evaluate the performance of the obtained parameters in both the frequency and time domains, time history displacement and acceleration transfer function of the top story of the structure are calculated for the model with and without considering the SSI effects.

New enhanced colliding body optimization algorithm based on a novel strategy for exploration

Researchers use the improvement strategies to enhance the current version of optimization algorithms to establish the right balance between local and global search phases to escape from a local minimum in some NP-hard problems. Colliding Bodies Optimization (CBO) and its enhanced version (ECBO) use the concept of mass and energy conservation law based on Newtonian Classic Physics. The quality of mentioned algorithm performance can be improved by changing how the bodies collide with each other. New enhanced colliding bodies Optimization (NECBO) moves all bodies towards the best solution in consecutive iterations to increase the chance of accessing a better solution and exploring all promised solution space. In this paper, the recently developed meta-heuristic algorithm, so-called Enhanced Colliding Bodies Optimization (ECBO), and one new proposed version of ECBO (called NECBO), including a new mechanism for finding new solutions, and two hybrid versions of both methods mentioned above (ECBO, NECBO) are utilized for the optimal design of sloped-roof rigid frames with non-prismatic members. The steel sloped portal frames, or steel gable frames, are frequently used in industrial buildings, gyms, schools, colleges, fire stations, storages, hangars , and many other low-rise structures. The gable frames' weight and shape are considerably affected by the member cross-sections' properties. The optimization of steel portal frames with tapered web members is a hard NP problem because of discrete variables, multiple local optimum, and corresponding constraints. The obtained results show the capability of NECBO and its hybridized version with ECBO in find a better solution than the conventional ECBO and CBO. • New enhanced colliding bodies’ optimization (NECBO) is developed by altering how bodies colide each other. • Enhanced Colliding Bodies Optimization (ECBO) and the new proposed NECBO include a new mechanism for finding new solutions. • The hybrid version of both ECBO and NECBO are utilized for optimal design of gable frames with tapered members. • Optimization of gable rigid frames with tapered web members is performed. • Results obtained from this study show the capability of NECBO and its hybridized version with ECBO.

A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine

Accurate photovoltaic power prediction is important to ensure the safety, stability, and economic operation of the power system after high photovoltaic demand. However, due to the intermittent and stochastic volatility characteristics of photovoltaic power, it is difficult to build satisfactory photovoltaic power prediction models. In this study, a similar day-based ultrashort-term multi-step ahead prediction model of photovoltaic power is proposed, which combines enhanced colliding bodies optimization (ECBO), variational mode decomposition (VMD), and a deep extreme learning machine (DELM). First, a new training data selection method based on grey correlation analysis and a Pearson correlation coefficient is proposed to find days that are similar to the predicted day. Second, the variational mode decomposition, based on enhanced colliding bodies optimization, is used to decompose the original photovoltaic power data into an ensemble of components with different amplitudes and frequencies, in which a new fitness function is designed based on a weighted-permutation entropy algorithm. Finally, the deep extreme learning machine network is employed to complete the prediction of each component, and the enhanced colliding bodies optimization algorithm is utilized again to obtain the optimal neuron numbers in hidden layers of the deep extreme learning machine; the ultimate prediction results of the photovoltaic power is obtained by reconstructing the prediction value of each component. The proposed model is tested with data from a real-world photovoltaic power station located in Xinjiang, China. The experimental results show that: (a) among all the competing models, the proposed model can achieve the highest multi-step prediction accuracy; (b) a qualitative breakthrough on the prediction accuracy improvement has been made by the proposed model in the 1-step and 2-step ahead prediction; (c) only a few parameters need to be tuned manually in the whole prediction process; the proposed model has good intelligence capabilities and can be easily applied and popularized in the practical projects.

A Hybrid Stochastic Algorithm with Domain Reduction for Discrete Structural Optimization

In recent years, many nature-inspired metaheuristic optimization algorithms have been proposed in an effort to develop efficient and robust algorithms. The drawback in most of them is the large number of simulations required to obtain good designs. To reduce the number of structural analyses to reach the best design, a new two-phase algorithm is proposed and evaluated. This hybrid algorithm is based on the well-known Harmony Search (HS) algorithm and recently developed Colliding Bodied Optimization (CBO). HS analyzes and improves one design in every iteration whereas CBO generates and analyzes a new population of designs in every iteration. Based on the observed behavior of these two algorithms, a Hybrid Harmony Search - Colliding Bodies Optimization (HHC) is proposed. The first phase of HHC uses the Improved Harmony Search (IHS) algorithm. A new design domain reduction technique is also incorporated in IHS that dramatically reduces the number of possible combinations of discrete variables. This improves the performance of the IHS algorithm. The second phase uses the Enhanced Colliding Bodies Optimization (ECBO). ECBO receives final designs from the first phase to enhance them further. This makes the second phase need fewer iterations in comparison with the ECBO alone. The performance of the proposed algorithms is evaluated using some benchmark discrete structural optimization problems, although the method is applicable to continuous-variable problems as well. The results show HHC with design domain reduction to be quite effective, robust, and needs a smaller number of structural analyses to solve optimization problems in comparison with IHS, ECBO, and some other metaheuristic optimization algorithms. HHC with design domain reduction is shown to be quite robust in the sense that different runs for a problem obtain the same final design. In comparison with HIS and ECBO, HHCD reduces the number of structural analyses to find the best design to less than half. This is an important feature that leads to better confidence in the final solution from a single run of the algorithm for a problem.

On the seismic collapse capacity of optimally designed steel braced frames

The main purpose of this study is to respond to an important question in the field of structural engineering: is the seismic collapse capacity of optimally designed concentrically steel braced frames acceptable or not? The present work includes two phases: performance-based design optimization and seismic collapse safety assessment. In the first phase, three nature-inspired metaheuristic algorithms, namely improved fireworks algorithm, center of mass optimization, and enhanced colliding-bodies optimization are employed to carry out the optimization task. In the second phase, seismic collapse capacity of the optimally designed concentrically steel braced frames is evaluated by performing incremental dynamic analysis and generating fragility curves. Two design examples of 5- and 10-story concentrically steel braced frames with two different topologies of braces are presented. The numerical results indicate that the center of mass optimization algorithm outperforms the other algorithms. However, all of the optimal designs found by all algorithms are of acceptable seismic collapse safety.

Optimum design of buckling-restrained braced frames

The current study investigated the optimum design of buckling restrained braced frames (BRBFs) subjected to seismic excitation. Nonlinear time history analysis was employed to examine the performance of the structures under seismic excitation. The objective function in the optimization problem was defined such that the weight of the structure and the amount of energy dissipated were simultaneously optimized. The search space of the optimization problem consists of discrete and continuous spaces due to the presence of BRBs. The metaheuristic salp swarm algorithm (SSA) and enhanced colliding bodies optimization (ECBO) algorithm were employed to determine the optimum design of the BRBFs having three and six stories. It was revealed that the weight of the BRBs had little influence on the total weight of the structure; however, BRBs could reduce the weight of the structure by maximizing energy dissipation. Furthermore, the ECBO algorithm produced a more efficient optimum design when compared to the SSA.

Optimal design of planar RC frames considering CO2 emissions using ECBO, EVPS and PSO metaheuristic algorithms

Nowadays reduction of greenhouse gas emissions is considered in the design and construction of reinforced concrete (RC) structures. One of the techniques for minimizing CO2 emissions is the use of optimization techniques at the structural design stage. For optimal design of RC structures, in addition to considering specifications of codes and construction material costs, strategies should also be applied for minimizing CO2 emission in the construction materials production. This study investigates the relationship between optimal cost and optimal carbon dioxide emission in design for RC frames of different heights by using an automatic computational process. The main objective functions are minimizing CO2 emissions and construction cost. For this purpose, three RC frames with four, eight and twelve stories, are modeled and optimized. Results indicate that by applying the optimization process, in buildings with low height the reductions of CO2 emissions are greater than in high-rise buildings. Three metaheuristic algorithms consisting of Enhanced Colliding Bodies Optimization (ECBO), Enhanced Vibrating Particles System (EVPS) and Particle Swarm Optimization (PSO) have been used to solve the examples. A comparison of the performance of these algorithms shows that in optimization of RC structures, which has many design variables and a large search space, the ECBO algorithm has higher search capability and results in better solutions than EVPS and PSO algorithms.

Simultaneous analysis and optimal design of truss structures via displacement method

In this paper, an efficient technique is proposed by displacement method of analysis and three metaheuristic algorithms consisting of the Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO) and Vibrating Particles System (VPS), for the simultaneous analysis and optimal design of truss structures. The presented method is applied to the minimum weight design of some planar and spatial truss structures. For examining the accuracy and efficiency of the proposed method, the problems are also designed by the same metaheuristic algorithms utilizing pure force method and pure displacement method as analysis tools (non-simultaneous) and the resulting structural weights are compared.

Optimal Design of Steel-Concrete Composite I-girder Bridges Using Three Meta-Heuristic Algorithms

Bridges are among very important structures in engineering, due to their rather high cost, and this is why optimization of these structures is a challenging problem. In this paper, optimal design of steel-concrete composite I-girder bridges is performed. Three recently developed meta-heuristic algorithms consisting of Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO) and Vibration Particle System (VPS) are utilized for the first time in the optimal design of steel-concrete I-girder bridges. Both continuous and discrete variables are utilized in the process of optimization. Performance and the convergence histories of these algorithms are compared. In order to have a suitable comparison between these algorithms with previous algorithms, PSO is used and results are displayed. This paper focuses on cost optimization the bridges. Furthermore constraints include all of requirements of the code of practice for design. The comparative study has shown that VPS algorithm has better performance than CBO and ECBO. However, all three algorithms act in a way that the final optimized design does not need the addition of the longitudinal stiffener.

Optimal Design of Double-Layer Domes Considering Different Mechanical Systems via ECBO

In this paper, a finite element model based on geometrical nonlinear analysis of large-scale double-layer domes and suspen-domes with pinned and rigid connections is presented. An optimal geometry and sizing design are performed using the enhanced colliding bodies optimization (ECBO) method. The length of the strut, the cable initial strain, the cross-sectional areas of the cables and steel elements, and height of domes are considered as design variables and the volume of each dome is taken as the objective function. A simple approach is defined to determine the configurations of the dome structures. This approach includes calculating the joint coordinates and formation of steel elements and cables. Numerical results show the robustness of ECBO algorithm. The efficiency of Lamella suspen-dome with pin-joint and rigid-joint connections is then explored and compared with double-layer Lamella dome to investigate the performance of these systems under dead and snow loading condition. Optimization process is performed via ECBO algorithm to demonstrate the effectiveness and robustness of the ECBO in finding optimal designs for different systems of domes.

Optimization of Tower Crane Location and Material Quantity Between Supply and Demand Points: A Comparative Study

Location optimization of tower crane as an expensive equipment in the construction projects has an important effect on material transportation costs. Due to the construction site conditions, there are several tower crane location optimization models. Appropriate location of tower cranes for material supply and engineering demands is a combinatorial optimization problem within the tower crane layout problem that is difficult to resolve. Meta-heuristics are popular and useful techniques to resolve complex optimization problems. In this paper, the performance of the Particle Swarm Optimization (PSO) and four newly developed meta-heuristic algorithms Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS) are compared in terms of their effectiveness in resolving a practical Tower Crane Layout (TCL) problem. Results show that ECBO performs better than other three methods in both cases.