Bang-Big Crunch Optimization Research Articles

Active suspension control using novel HB3C optimized LQR controller for vibration suppression and ride comfort enhancement

This article addresses the optimization of a Vehicle Active Suspension System (VASS) through the application of a Linear Quadratic Regulator (LQR) controller. The primary objective is to enhance ride comfort and ensure vehicle stability by addressing the divergent needs of vibration control. The research identifies key issues in existing optimization algorithms, namely, the exploration stage inefficiency in Big Bang Big Crunch Optimization (B3C) and the slow convergence rate in Coyote Optimization (CO). To overcome these challenges, a novel hybrid algorithm, Hybrid Coyote optimization based Big Bang Big Crunch (HB3C), is proposed. The research objective is to optimize the LQR weighting matrices using the HB3C algorithm, aiming for improved ride comfort and vehicle safety. The problem statement involves the inadequacies of existing algorithms in addressing the exploration and convergence issues. The motivation lies in enhancing the efficiency of VASS through optimal control, leading to better ride comfort and safety. The methodology involves integrating CO within a loop with B3C to compute the optimum reduction rate for the algorithm. Since, B3C algorithm’s success is highly dependent on selecting the ideal reduction rate. This hybrid approach is then applied to optimize the existing LQR weighting matrices. The results are evaluated in terms of time domain and frequency domain response analysis, with a focus on ride comfort based on ISO 2631-1 standards. The study demonstrates a maximum reduction of approximately 74% achieved by the optimized HB3C-LQR controllers.

Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System

This study focuses on the design of a robust PD gain-scheduling controller (PD-GS-C) for an unstable SISO (single-input, single-output) magnetic levitation system with two electromagnets (MLS2EM). Magnetic levitation systems offer various advantages, including friction-free, reliable, fast, and cost-effective operations. However, due to their unstable and highly nonlinear nature, these systems require sophisticated feedback control techniques to ensure optimal performance and functionality. To address these challenges, in this study, we derive the nonlinear state-space mathematical model of the MLS2EM and linearize it around five different operating points. The PD-GS-C controller aims to stabilize the system and improve steady-state control error. The strategy for obtaining the PD controller gains involves a parameter space technique, which specifies performance requirements. This technique results in ranges of proportional (KP) and derivative (KD) gains that are used by the PD-GS-C structure. To optimize the controller’s performance further, we utilize the big bang–big crunch optimization technique (BB-BC) to determine the optimal PD gains within the specified ranges. The optimization process focuses on achieving optimal performance in terms of a specific performance index function. This function quantifies the system’s time-domain step response criteria, which include minimizing overshoot percentage, settling time, and rising time. The index function is inversely proportional to the desired performance criteria, meaning that the goal is to maximize the index function to optimize the system’s performance. To validate the effectiveness and viability of the proposed strategy, we conducts MATLAB simulations and real-time experiments. The simulations and experimental findings serve to demonstrate the controller’s performance and verify its capabilities in stabilizing the MLS2EM magnetic levitation system.

VGG16 feature selection using PCA-big bang big algorithm

In the recent decade, plant disease classification using convolution neural networks has proven to be superior because of its ability to extract key features. Obtaining the optimum feature subset with the necessary discriminant information is challenging. The main objective of this paper is to design an efficient hybrid plant disease feature selection approach and validate it on standard image datasets. The raw input image features were transformed into 8192 learned features by employing the VGG16. To reduce the training time and enhance classification accuracy, the dimensionality reduction technique Principal Component Analysis (PCA) is integrated with the big bang-big crunch (BBBC) optimization algorithm. The PCA-BBBC feature selection method reduces computing time by eliminating unnecessary and redundant features. The proposed approach was evaluated on plant diseases and benchmarked image datasets. Experimental results reveal that the Artificial Neural Network (ANN) classifier integrated with the VGG16-PCA-BBBC approach enhanced the performance of the classifier. The proposed approach outperformed the VGG16-PCA-ANN method and other popular image classification techniques. For the rice disease dataset, the proposed hybrid approach reduced the VGG16 extracted 8192 deep features to 200 relevant principal components. The recommended reduced features were used for training ANN. The test dataset was classified by ANN with an accuracy of 99.12%. Experimental results demonstrate that the proposed approach improved the performance of the classifier and accurately labeled image and plant diseases datasets aiding farmers to adopt remedial measures.

Vibration Control and Ride Comfort Analysis of a Full Car with a Driver Model Using Big-Bang Big-Crunch Optimized FLC

The main motive of the active suspension system is to enhance the ride comfort by suppressing the vibrations induced due to road irregularities. The actuator is getting more significance in the automotive industry due to its superior ride comfort for passengers. In this study, an eight Degrees of Freedom (DOF) full car with a driver model is considered for the ride comfort analysis (ISO2631 Standard) and vibration control. The real road terrain is not uniform in nature. Hence an effective intelligent control technology is required to improve the quality of travel. Initially, a conventional Proportional Integral and Derivative (PID) controller is designed for the system. Then a Fuzzy Logic Controller (FLC) is designed for the 8 DOF vehicle model to control the unwanted vibration produced in the vertical and angular directions. The FLC is designed with suitable firing rules, membership functions, and scaling factors using MATLAB/Simulink 2019 environment. The performance of FLC is further enhanced by the Big Bang Big Crunch (B3C) optimization technique, and it can suppress vibrations when the vehicle is travelling over bumpy and imperfect random road terrain as per ISO 8608. The Root Mean Square (RMS), Frequency Weighted RMS (FWRMS), and Vibration Dose Value of seat acceleration are the performance indices to investigate the potency of B3C-tuned FLC against the PID controller, FLC and passive suspension system.

BBBC-DDRL: A hybrid big-bang big-crunch optimization and deliberated deep reinforced learning mechanisms for cyber-attack detection

Currently, the wireless networks are increasingly used in all kinds of applications due to their adaptability, flexibility, and easy-access. Without a predetermined architecture, wireless sensor networks are often deployed and unmanaged. Due to these traits, the wireless networks are more vulnerable to attacks, and an opponent can easily track traffic because the data are transmitted over the air. In the existing studies, a different types of attack detection methodologies are used for intrusion identification and classification. But, they having problems in terms of inaccurate prediction, high false positives, and lack of reliability. Thus, the proposed research work intends to implement a sophisticated and novel cyber-security model for safeguarding wireless networks. Here, the Graph Referencing Normalization (GRN) algorithm is specifically used to preprocess the datasets based on the normalization vector. Then, the cutting-edge Big-Bang Big-Crunch (BBBC) optimization technique is employed to obtain the relevant features from the normalized data for analyzing the characteristics of attacks. The Deliberate Deep Reinforced Learning (DDRL) classification process is used to predict the normal and attacking data based on the features. By using the GRN, BBBC and DDRL mechanisms, the intrusions are accurately predicted from the cyber-datasets. To assess the performance of this model, the Matlab simulation tool has been used, where the results are validated with the use of different parameters like precision, recall, f1-score, and etc. The estimated results indicate the average attack detection rate is improved up to 99% for all datasets, when compared to the other existing security models.

Modelling and Analysis of the Cone Coupling Problem Using Optimization

A coupling is a mechanism that transmits operative power between two shafts that are revolving at different speeds. A coupling connects two shafts at their ends and can slip or fail depending on the torque limit. It is an essential component of any power transmission system and may survive for a very long time if properly designed and maintained. This study's current research, a newly developed optimization algorithms are used to minimize the volume of cone coupling. The current study presented here compares modern metaheuristic methods for optimizing the design of the cone coupling problem. The algorithms used are particle swarm optimization (PSO), crow search algorithm (CSA), enhanced honeybee mating optimization (EHBMO), Harmony search algorithm (HSA), Krill heard algorithm (KHA), Pattern search algorithm (PSA), Charged system search algorithm (CSSA), Salp swarm algorithm (SSA), Big bang big crunch optimization (B-BBBCO), Gradient based Algorithm (GBA). The performance of these algorithms is assessed both statistically and subjectively. The algorithms' performance is evaluated quantitatively and qualitatively using consistency, simplicity and quality. The experimental results on the cone clutch problem shows that PSO produces greater results than EHBMO, whereas CSA and BBCO produce approximately identical results.

A swarm optimizer with modified feasible-based mechanism for optimum structure in steel industry

This study proposes a swarm optimizer with a modified feasible-based mechanism approach for finding an optimum design for steel frames. The proposed optimization approach addresses the problem of stagnation possibility in the traditional particle swarm optimization in which none of the particles tries to explore a position better than the previous best position for multiple numbers of iterations. This method is based on accelerated particle swarm optimization and big bang–big crunch optimization algorithms. In addition, a modified feasible-based mechanism is used to correct the particle’s position. The new method’s performance is evaluated by solving two structural problems to minimize the weight of steel frames. The results show that the optimized designs obtained by the proposed algorithm are better than those found by the competing algorithms from the literature.

A Feature Reduced Intrusion Detection System with Optimized SVM Using Big Bang Big Crunch Optimization

A Feature Reduced Intrusion Detection System with Optimized SVM Using Big Bang Big Crunch Optimization

Order Diminution of LTI Systems Using Modified Big Bang Big Crunch Algorithm and Pade Approximation with Fractional Order Controller Design

In this paper, a novel approach is proposed for the reduced order modelling of linear time invariant (LTI) systems. The proposed approach is a combination of modified Big bang big crunch (BBBC) optimization algorithm and Pade approximation technique. The beauty of the proposed approach is that the selection of solution space for BBBC algorithm is not entirely random, but structured via the use of Pade approximation approach. Hence, two principal criticisms of soft computing algorithms, i.e., random choice of solution space and larger simulation time are averted in the proposed technique. The proposed technique is substantiated via four different numerical examples from literature and compared with existing model order reduction (MOR) techniques. The concept of controller design is introduced via application of fractional order internal model control technique for load frequency control of power systems. Further, BBBC algorithm is employed to tune a boiler loop in power station. The results convey the efficiency and powerfulness of the proposed technique.

Design of Fractional Order PID (FOPID)for Load Frequency Control Via IMC & Grey Wolf Algorithm (GWO), for a Non-Reheated Power System by Comparing with the Big Bang Big Crunch (BBBC) Optimization

In this paper work deals about the application of Grey Wolf Optimizer (GWO) for optimization of fractional order PID (FOPID) controlling device to the frequency disturbance, of system load in the one (or) single area non re-heated electrical system and also comparison to the non re-heated BBBC optimization outputs. In this BBBC optimization we have the two bounding cases (low & upper), they are before and after the perturbation cases. And also we observed that the BBBC output responses. After finding the BBBC outputs we observed that the settling time value of load frequency of BBBC is more when compared with the GWO. This problem is resolved by designing of FOPID via GWO algorithm. The Grey Wolf Optimization is well known meta-heuristic algorithm and has been previously used for optimization of various conventional PID and FOPID controllers. In this paper the GWO is used for optimization of FOPID controller to the load frequency variation in the electrical system for non reheated turbine electrical system .the execution outputs of the proposed controlled method also validated to the other existing techniques.

Optimal PID control of spatial inverted pendulum with big bang–big crunch optimization

As the extension of the linear inverted pendulum ( LIP ) and planar inverted pendulum ( PIP ) , this paper proposes a novel spatial inverted pendulum ( SIP ) . The SIP is the most general inverted pendulum ( IP ) than any existing IP. The model of the SIP is presented for the first time. The SIP inherits all the characteristics of the LIP and the PIP, which is a nonlinear, unstable and underactuated system. The SIP has five degrees of motion freedom and three control forces. Thus, it is a multiple-input and multiple-output ( MIMO ) system with nonlinear dynamics. To realize the spatial trajectory tracking of the SIP, the control structure with five PID controllers will be designed. The parameter tuning of the multiple PIDs is a challenging work for the proposed SIP model. To alleviate the difficulties of the parameter tuning for the multiple PID controllers, optimal PIDs can be achieved with the help of Big Bang-Big Crunch ( BBBC ) optimization. The BBBC algorithm can successfully optimize the parameters of the multiple PID controllers with high convergence speed. The optimization performance index of the BBBC algorithm is compared with that of the particle swarm optimization ( PSO ) . Simulation results certify the rightness and effectiveness of the proposed control and optimization methods.

Implementation and fine-tuning of the Big Bang-Big Crunch optimisation method for use in passive building design

Passive building design involves consideration of a number of design variables which can be optimised with respect to some design objective. The use of optimisation methods in building design is well established but the implementation of the Big Bang-Big Crunch algorithm to passive building design is still as yet unexplored in detail. The aim of this paper is to demonstrate the usefulness of the method as well as to fine-tune its characteristic parameters for its efficient use in building design applications. Two building design scenarios are used to have more than one test case and the number of design variables are limited to an extent that an enumeration search can be performed in a reasonable time frame from which the optimum can be precisely established. The Big Bang-Big Crunch algorithm is used and validated against the true solution from the enumeration search. Results show that the α and β parameters should be set to 0 and around 0.8 respectively. The ratio of the sample size to the number of design variables should ideally be between 2.6 and 2.9 for the algorithm to remain efficient and at the same time successful. The paper also discusses the computational efficiency gain of the Big Bang-Big Crunch algorithm compared to a full enumeration search. Computational savings of more than 90% are possible.

A Fractional Order Control Strategy for LFC via Big Bang Big Crunch & Grey Wolf Optimization Algorithms

The Grey Wolf Optimization (GWO) is well known meta-heuristic algorithm and has been previously used for optimization of various conventional PID and FOPID controllers. This paper deals with the application of Grey Wolf Optimizer (GWO) algorithm and internal model control (IMC) for optimization of fractional order PID (FOPID) controller parameters to the load disturbance of system. This is applicable for one (or) single area non reheated electrical system. The simulation results are compared with the non re-heated Big Bang Big Crunch (BBBC) optimization outputs. In this paper, BBBC optimization has the two bounding cases (lower & upper), they are before and after the perturbation cases. Also it is observed that in case of the BBBC output responses, the settling time value of load frequency is more when compared with the GWO. From the simulation results it is concluded that GWO out performs as compared with BBBC as it produces less error and settling time.

Efficient Utilization of IaaS Cloud using Adaptive Evolution Based Technique

In the present era of technology and innovation, computing provides as a service. The service-oriented computing paradigm is a subset of utility and distributed computing paradigm. Hardware specification, platform, and application provide a service to the users in a different time zone. Resource utilization and request completion time is the prime focus of the service providers and consumers. In this work, we proposed a nature-inspired evolution and astrology science-based approach. The Big-Bang Big-Crunch optimization method based task allocation technique focuses on efficient resource utilization of IaaS (Infrastructure as a Service) cloud. We focused on three performance metrics, which may be user-oriented and service provider oriented. Performance metrics measure in terms of average resource utilization cost. Average Finish time, an average start time, and operational cost (customer-oriented) performance metrics are consumer-centric. The simulation performs using five cloud resources, six cloud configurations, and user-defined population size, and number iterations. The selection operator includes a fitness function that depends on estimated resource cost in the duration of execution. BB-BC cost-aware model provides optimal resource utilization, and minimum average finish time, and minimum average start time (ms) of cloudlets. The results exhibit that the proposed astrology, evolution based meta-heuristic approach outperforms the static (first come first serve (FCFS)), dynamic, and meta-heuristic (Genetic cost-aware, Genetic execution time aware and Particle Swarm optimization) approaches.

Fusion of inverse optimal and model predictive control strategies

In this study, model predictive control (MPC) and inverse optimal control (IOC) approaches are merged with each other and a new control strategy is evolved. The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications.

Local Coordinate Exhaustive Search Hybridization Enhancing Big Bang-Big Crunch and Particle Swarm Optimization Algorithms

In general, all of the hybridized evolutionary optimization algorithms use “first diversification and then intensification” routine approach. In other words, these hybridized methods all begin with a global search mode using a highly random initial search population and then switch to intense local search mode at some stage. The population initialization is still a crucial point in the hybridized evolutionary optimization algorithms since it can affect the speed of convergence and the quality of the final solution. In this study, we introduce a new approach by creating a paradigm shift that reverses the “diversification” and then “intensification” routines. Here, instead of starting from a random initial population, we firstly find a unique starting point by conducting an initial exhaustive search based on the coordinate exhaustive search local optimization algorithm only for single step iteration in order to collect a rough but some meaningful knowledge about the nature of the problem. Thus, our main assertion is that this approach will ameliorate convergence rate of any evolutionary optimization algorithms. In this study, we illustrate how one can use this unique starting point in the initialization of two evolutionary optimization algorithms, including but not limited to Big Bang-Big Crunch optimization and Particle Swarm Optimization. Experiments on a commonly used benchmark test suite, which consist of mainly rotated and shifted functions, show that the proposed initialization procedure leads to great improvement for the above-mentioned two evolutionary optimization algorithms.

ANN Model Identification: Parallel Big Bang Big Crunch Algorithm

This paper proposes a modification to existing big bang big crunch optimization algorithm that uses the concept of more than one population. In this the search begins with all the populations independently in parallel and as the algorithm proceeds the local best of the individual populations interact with global best to avoid local minima. In order to validate the proposed approach the authors have identified two models one from control field namely rapid battery charger and second a rating system for institutes of higher learning and compared its results with simple BB-BC based approach .The author further compared results of the proposed approach with the results of other recent soft computing based algorithms for ANN model identification. The proposed algorithm outperformed all of the other 7 algorithms in terms of MSE and convergence time.

Development of optimum cold-formed steel beams for serviceability and ultimate limit states using Big Bang-Big Crunch optimisation

Cold-formed steel (CFS) elements are increasingly used as main structural members in modern construction practice. While flexibility of CFS cross-sectional shape allows achieving higher load carrying capacities by using more efficient shapes, obtaining optimum design solutions can be a challenging task due to end-use constraints and complex behaviour of CFS elements controlled by local, global and distortional buckling modes. This study aims to develop a practical methodology for optimum design of CFS beam sections with maximum flexural strength and minimum deflection under ultimate and serviceability load conditions, respectively, in accordance with Eurocode 3 by taking into account manufacturing and end-use design constrains. Population-based Big Bang–Big Crunch Optimisation method is employed to obtain optimum design solutions for twelve different CFS cross-sectional prototypes. To verify the flexural strength and stiffness of the optimum beam sections, detailed nonlinear finite element (FE) models are developed using ABAQUS by considering both material nonlinearity and initial geometrical imperfections. It is shown that the optimised sections based on serviceability limit state (SLS) and ultimate limit state (ULS) can provide, respectively, up to 44% higher effective stiffness and 58% higher bending moment capacity compared to a standard lipped channel beam section with the same plate width and thickness. Using plain channel and folded-flange sections generally leads to the best design solutions for SLS and ULS conditions, respectively. Finally, the results of detailed FE models are used to evaluate the adequacy of EC3 proposed procedures to estimate CFS beam capacity and deflection at ULS and SLS, respectively.

Hybrid parliamentary optimization and big bang-big crunch algorithm for globaloptimization

Researchers have developed different metaheuristic algorithms to solve various optimization problems. The efficiency of a metaheuristic algorithm depends on the balance between exploration and exploitation. This paper presents the hybrid parliamentary optimization and big bang-big crunch (HPO-BBBC) algorithm, which is a combination of the parliamentary optimization algorithm (POA) and the big bang-big crunch (BB-BC) optimization algorithm. The intragroup competition phase of the POA is a process that searches for potential points in the search space, thereby providing an exploration mechanism. By contrast, the BB-BC algorithm has an effective exploitation mechanism. In the proposed method, steps of the BB-BC algorithm are added to the intragroup competition phase of the POA in order to improve the exploitation capabilities of the POA. Thus, the proposed method achieves a good balance between exploration and exploitation. The performance of the HPO-BBBC algorithm was tested using well-known mathematical test functions and compared with that of the POA, the BB-BC algorithm, and some other metaheuristics, namely the genetic algorithm, multiverse optimizer, crow search algorithm, dragonfly algorithm, and moth-flame optimization algorithm. The HPO-BBBC algorithm was found to achieve better optimization performance and a higher convergence speed than the above-mentioned algorithms on most benchmark problems.

A Coordinated Real-Time Voltage Control Approach for Increasing the Penetration of Distributed Generation

A major concern associated with the connection of distributed generators (DGs) to distribution systems is the voltage rise problem. A solution introduced in many voltage control schemes is DG active power curtailment. However, this measure has a negative effect on both DG revenue and DG investment. This paper proposes the use of existing system resources to mitigate the voltage rise problem. Based on the big bang-big crunch optimization and the reactive power compensation method, the new technique controls the distribution system voltage profile while avoiding DG active power curtailment. The proposed method minimizes voltage deviations by determining the regulator taps and reactive power contributions of both the capacitors and the DGs. The approach is implemented online by a central control unit that optimally determines appropriate control decisions and then communicates them to the voltage regulators, capacitors, and DGs. Eliminating the voltage deviation problem associated with the network installation of DGs provides a cost-effective means of increasing DG penetration. The new algorithm was implemented and tested on the 33-bus test feeder. The results demonstrate the effective control of the voltage profile and fast convergence speed provided by the developed technique.