Colliding Bodies Optimization Algorithm Research Articles

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.

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

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.

Special issue on International Conference on Computing and Communication Networks

Special issue on International Conference on Computing and Communication Networks

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%.

Inverse analysis of strain distributions sensed by distributed fiber optic sensors subject to strain transfer

Strains measured by distributed fiber optic sensors with protective coatings can be different from the real strains in host matrix due to strain transfer effect. This study presents a novel inverse method to determine the real strain distributions in host matrix using strain distributions measured from distributed fiber optic sensors. Two different scenarios of strain distributions in host matrix are investigated, which have known and unknown types of strain fields, respectively. Three representative metaheuristic algorithms are respectively utilized in the inverse analysis, which are colliding bodies optimization algorithm, particle swarm optimization algorithm, and genetic algorithm. The performance of the proposed method was evaluated by analytical case studies and experimental testing of a prestressed concrete beam subjected to four-point bending. The results demonstrated high accuracy and efficiency of the proposed method. This study is expected to greatly advance distributed fiber optic sensing capabilities in measuring strain distributions for structural health monitoring.

Enhanced Colliding Bodies Optimization Towards the Solution of Economic Load Dispatch Problem

This paper proposes application of enhanced colliding bodies optimization algorithm (ECBO) for solving the economic load dispatch problem, which aims to get minimum generation cost through economic scheduling of generating unit. The algorithm is a novel efficient optimization algorithm, and its idea is attained from collisions, which are one-dimensional between two or more bodies. Each agent involved in this collision is shaped as a body having defined mass and defined velocity. To analyze the performance of the proposed method, it is tested on four different test systems consisting of 3, 5, 13, and 18 generating units. Both convex and non-convex fuel cost function have been considered. Numerical results have been compared with various well-known algorithms. A significant improvement in the results has been observed. It has been farther noted that an average 5.1% better results were provided by ECBO in terms of generation cost compared to other algorithms. Moreover, the simulation time is also improved. Besides this, Kruskal-Wallis non-parametric test has been performed.

Colliding bodies algorithm with adaptive parameter adjustment strategy

Colliding Bodies Optimization (CBO) is a new meta-heuristic algorithm that uses collisions between objects to move to a better position so that the solution tends to be an optimal solution. Aiming at the shortcomings of CBO algorithm's poor optimization accuracy and prone to evolutionary stagnation in the later stage of iterations, this paper proposes a new Colliding Bodies Optimization algorithm with adaptive adjustment strategy. Firstly, a set of initial solutions of colliding bodies are generated by the good-point set strategy to improve the stability of the algorithm solution. Secondly, we adjust the linear strategy of the control parameters to a nonlinear strategy and combine it with the fitness value to form an adaptive parameter adjustment strategy. Finally, in order to make the algorithm escape from the local optimal, a mutation operation is performed on the position of the object. In order to test the effectiveness of the proposed algorithm, experiments were conducted on 23 benchmark functions with it and the comparison algorithm. The experimental results show that the performance of the proposed algorithm is better than other experimental algorithms.

Cohort intelligence with self-adaptive penalty function approach hybridized with colliding bodies optimization algorithm for discrete and mixed variable constrained problems

Recently, several socio-/bio-inspired algorithms have been proposed for solving a variety of problems. Generally, they perform well when applied for solving unconstrained problems; however, their performance degenerates when applied for solving constrained problems. Several types of penalty function approaches have been proposed so far for handling linear and non-linear constraints. Even though the approach is quite easy to understand, the precise choice of penalty parameter is very much important. It may further necessitate significant number of preliminary trials. To overcome this limitation, a new self-adaptive penalty function (SAPF) approach is proposed and incorporated into socio-inspired Cohort Intelligence (CI) algorithm. This approach is referred to as CI–SAPF. Furthermore, CI–SAPF approach is hybridized with Colliding Bodies Optimization (CBO) algorithm referred to as CI–SAPF–CBO algorithm. The performance of the CI–SAPF and CI–SAPF–CBO algorithms is validated by solving discrete and mixed variable problems from truss structure domain, design engineering domain, and several problems of linear and nonlinear in nature. Furthermore, the applicability of the proposed techniques is validated by solving two real-world applications from manufacturing engineering domain. The results obtained from CI–SAPF and CI–SAPF–CBO are promising and computationally efficient when compared with other nature inspired optimization algorithms. A non-parametric Wilcoxon’s rank sum test is performed on the obtained statistical solutions to examine the significance of CI–SAPF–CBO. In addition, the effect of the penalty parameter on pseudo-objective function, penalty function and constrained violations is analyzed and discussed along with the advantages over other algorithms.

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.

Parallel system reliability analysis with a CECBO algorithm

The existing engineering structural system is featured by complicated limit state functions of high nonlinearity, resulting in the conventional reliability analysis approach that is unable to evaluate these problems accurately or even unavailable. Various analytical methods have been presented by researchers over the past decades. However, these methods have difficulties in striking a balance between accuracy and computational efficiency. This paper proposes a relatively efficient and accurate procedure to estimate the failure probability of the parallel system based on the FORM and a new metaheuristic algorithm, chaotic enhanced colliding bodies optimization algorithm (CECBO). The CECBO has shown robustness, high accuracy, and implementation simplicity for solving optimization problems from previous work. Thus, the CECBO is employed to search the point of maximum likelihood (PML), a point that yields the highest value of the probability density function in the failure domain, through solving an optimization model and then approximates the failure domain of the parallel system to estimate the failure probability accurately. The results of the test examples indicate that the proposed procedure is promising for the estimation of the failure probability of a parallel system in terms of accuracy and efficiency.

Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm

The resilient modulus (MR) of road materials is one of the most important parameters in the analysis and design of pavement. This parameter is used in both empirical methods and mechanistic-empirical methods as the main parameter for expressing the stiffness and behavior of road construction materials. To determine this parameter in the laboratory, it is necessary to perform a dynamic tri-axial loading test under various confining and deviator stresses, which is a time- and cost-intensive approach. In this paper, a support vector machine (SVM) hybridized with the colliding bodies optimization (CBO) algorithm was used to model the MR of non-cohesive subgrade soils, unbound subbase materials, and subgrade-subbase materials. For this purpose, a dataset was collected from published literature, which included 190 distinctive data records on 13 different types of subbase materials and 272 distinctive data records on 18 different types of non-cohesive subgrade materials. To develop and validate the predictive models for MR, 75% of the data records were selected as training dataset while the remaining 25% were selected as testing dataset. The input variables in all the models included maximum dry density, uniformity coefficient, curvature coefficient, percent passing No. 200 sieve, confining stress, and deviator stress. For the models developed for subbase, subgrade, and subbase-subgrade materials, the R2 values obtained for the testing dataset were 0.988, 0.979, and 0.972, respectively. The results of this study also indicate that the SVM-CBO method is more accurate than the ANN method in predicting the MR of non-cohesive subgrade soils and unbound subbase materials. Moreover, a series of gamma tests were conducted to evaluate the most effective parameters for the prediction of the MR. For the subbase-subgrade materials, percent passing No. 200 sieve and confining stress were found to be the least and the most effective variables. A parametric analysis was also done to study the effects of each input variable on the MR.

A rotation learning-based colliding bodies optimization algorithm

In this paper, we propose an enhancement algorithm of colliding bodies optimization based on rotation learning, named by RBL-CBO. Firstly, we use the rotation-based learning to search for any point in the rotating space by adjusting the rotation angle, thereby improving the ability of the proposed algorithm jump out of the local optimum. Next, we leverage the sinusoid-based nonlinear adjustment strategy to modify the control parameters to improve the calculation accuracy of the proposed algorithm. Finally, we process the cross-boundary object by the mirroring strategy. We conduct extensive experiments to test the performance of the proposed algorithm. In simulation-based experiments, 23 benchmark functions are used to compare RBL-CBO algorithm with the CBO, DE, BBO, PSO and GSA algorithm. The experimental results demonstrate that the proposed RBL-CBO algorithm is superior to the other comparison algorithms, while the RBL-CBO algorithm is at least 20% higher than the CBO algorithm in terms of the accuracy of solving function optimization problem.

Nonlinear vibration and geometric optimization of nanocomposite multilayer organic solar cell under wind loading

This paper presents an analytical approach on the nonlinear vibration of nanocomposite multilayer organic solar cell (NMOSC) subjected to the combination of wind load and uniform temperature change. The NMOSC comprises of five layers, which are made of Al, P3HT:PCBM, PEDOT:PSS, Graphene and Glass. Compared to common models of the NMOSC, ITO is replaced by graphene to control the output colors of NMOSC. The nonlinear motion equations and the compatibility equation are deriving by taking into account the effect of viscous damping, von Karman nonlinearity terms, elastic foundations and initial imperfection. The formula of the wind load is improved for consideration in the general case that the direction of the wind load is not just perpendicular to the structure. Four edges of the NMOSC are assumed to be simply supported and immovable in the transverse plane. The Galerkin and Runge – Kutta methods are applied to obtain the dynamic response and the natural frequency of the NMOSC. Four optimization algorithms (Bees Algorithm, Basic differential evolution algorithm, enhanced colliding bodies optimization algorithm, social group optimization algorithm) are used to determine the maximum value of natural frequency of the NMOCS, which depends on nine variables including the geometrical parameters, elastic foundations stiffness and temperature increment. In the numerical results, the effects of elastic foundations, initial imperfection, viscous damping ratio, temperature increment, wind load and the length to width ratio on the dynamic response and natural frequency are considered in details. The maximum values of the natural frequency of the NMOSC with four algorithms are obtained and compared. The results show that the optimal value obtained by four algorithms is close to each other. However, the calculation time with enhanced colliding bodies optimization algorithm is the fastest and with Bees Algorithm is the longest of all.

Optimization of Interplanetary Trajectories Using the Colliding Bodies Optimization Algorithm

In this paper, a recent physics-based metaheuristic algorithm, the Colliding Bodies Optimization (CBO), already employed to solve problems in civil and mechanical engineering, is proposed for the optimization of interplanetary trajectories by using both indirect and direct approaches. The CBO has an extremely simple formulation and does not depend on any initial conditions. To test the performances of the algorithm, missions with remarkably different orbital transfer energies are considered: from the simple planar case, as the Earth-Mars orbital transfer, to more energetic ones, like a rendezvous with the asteroid Pallas.

A New Nodal Stress Recovery Technique in Finite Element Method Using Colliding Bodies Optimization Algorithm

In Finite Element Method (FEM), the stress components are calculated within the elements firstly, and then these components are recovered to the nodes. For the recovery process, there are several well-known methods in which the increase of their accuracy imposes additional costs into the problem. In this paper, a new nodal stress recovery technique is proposed in which Colliding Bodies Optimization (CBO) Algorithm fits an appropriative function for nodal stress fields. The CBO employs this function to compute the stress components in the nodal coordinates. Therefore, a particular model to stress fields and its components will be available. It can be considered as a connection between analytical approaches and numerical methods, providing benefits of both categories. Finally, the accuracy, efficiency, and applicability of the new technique are investigated employing three diverse examples.

Chaotic enhanced colliding bodies optimization algorithm for structural reliability analysis

It is of extreme importance to assess the failure probability and safety level of structural system in structural design. Nowadays, many researchers presented several approaches for structural reliability analysis, such as the first-order reliability method, Monte Carlo simulation, and the meta-heuristic algorithm. The meta-heuristic algorithm is not only efficient to solve global optimization problems but also shown to be an effective tool for structural reliability analysis. A recent meta-heuristic optimization approach, enhanced colliding bodies optimization, has emerged as a relatively simple implementation with a fast convergence speed. Chaos theory is characterized by its ergodicity, pseudo-randomness, and irregularity. This article thus presents a novel approach introducing chaotic maps into the enhanced colliding bodies optimization algorithm to promote the performance of convergence, named as chaotic enhanced colliding bodies optimization algorithm. The proposed algorithm uses chaotic maps to change the generation pattern of particles and improve convergence characteristics. A procedure based on the effective use of the represented chaotic enhanced colliding bodies optimization is then applied in structural reliability analysis. A variety of numerical and structural problems are tested in this article to demonstrate that the given method actually improves the performance of enhanced colliding bodies optimization in convergence as well as the accuracy for reliability analysis compared with the other methods existing in the literature.

Optimal placement of distributed generation using colliding bodies optimization

The present paper foremost objective is to resolve best practicable location of solar photovoltaic distribution generation (DG) of several cases using different distribution load power factors and to analyze power loss reduction. This objective achieved by a recent developed method, the so called colliding bodies’ optimization algorithm, to perceive optimum location. Performances of colliding bodies’ optimization algorithm have been evaluated and compared with other search algorithms. The execution to test viability and efficiency, the proposed collid-ing bodies’ optimization is simulated on standard IEEE 38 bus radial distribution networks. The acquired outcome from colliding bodies Optimization algorithm exhibits the possible location of distributed generation through different pre assumed load power factors compared to the other stochastic search bat and genetic algorithm.

Structural optimization of jacket supporting structures for offshore wind turbines using colliding bodies optimization algorithm

SummaryConsidering the size and dimension of offshore wind turbine structures, structural optimization of such structures, notwithstanding being outstandingly fruitful, is a tedious task. Nonetheless, in this paper, a metaheuristic algorithm named as colliding bodies optimization is employed when investigating the optimal design of jacket supporting structures for offshore wind turbines. The OC4 reference jacket is considered as the case study, validating the outcomes of this research. To do so, MATLAB is utilized in modeling the structure. The structural optimization is then performed when both ultimate limit state and frequency constraints are being considered. During the optimization process, the weight of the structure is approximately halved, and its first and second frequencies are kept within the considered soft–stiff range (0.21–0.32 Hz).