Bang-Big Crunch Optimization Algorithm Research Articles

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

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.

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.

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.

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.

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.

Big Bang-Big Crunch Optimization Algorithm for Solving the Uncapacitated Facility Location Problem

The big bang–big crunch (BB–BC) algorithm has been proposed as a new optimization method based on the big bang and big crunch theory, one of the theories of the evolution of the universe. The BB-BC algorithm has been firstly presented to solve the optimization problems with continuous solutions space. If the solution space of the problem is binary-structural, the algorithm must be modified to solve this kind of the problems. Therefore, in this study, the BB-BC method, one of the population-based optimization algorithms, is modified to deal with binary optimization problems. The performance of the proposed methods is analyzed on uncapacitated facility location problems (UFLPs) which are one of the binary problems used in literature. The well-known small and medium twelve instances of UFLPs are used to analyze the performances and the effects of the control parameter of the BB-BC algorithm. The obtained results are comparatively presented. According to the experimental results, the binary version of the BB-BC method achieves successful results in solving UFLP in terms of solution quality.

Automated selection of optimal material for pressurized multi-layer composite tubes based on an evolutionary approach

Decision making on the configuration of material layers as well as thickness of each layer in composite assemblies has long been recognized as an optimization problem. Today, on the one hand, abundance of industrial alloys with different material properties and costs facilitates fabrication of more economical or light weight assemblies. On the other hand, in the design stage, availability of different alternative materials apparently increases the complexity of the design optimization problem and arises the need for efficient optimization techniques. In the present study, the well-known big bang–big crunch optimization algorithm is reformulated for optimum design of internally pressurized tightly fitted multi-layer composite tubes with axially constrained ends. An automated material selection and thickness optimization approach is employed for both weight and cost minimization of one-, two-, and three-layer tubes, and the obtained results are compared. The numerical results indicate the efficiency of the proposed approach in practical optimum design of multi-layer composite tubes under internal pressure and quantify the optimality of different composite assemblies compared to one-layer tubes.

Memory-enriched big bang–big crunch optimization algorithm for data clustering

Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang-big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory based scheme as compared to its similar heuristic techniques. Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets. The experimental results show the significant superiority of the proposed method over the similar algorithms.

A BB-BC based Approach for Ambulance Deployment

Background/objectives: Punjab State's Department of Family Welfare and Health has extended Emergency Response Service (ERS) to all the citizens of the state free of cost at all times since March 2011. Under the service 240 ambulances were positioned across the state. The author intends to cover maximum area with given ambulances. Methods/Statistical Analysis: the aim of the paper is to improve the coverage of ERS. To do so, the author surveys the deployment methods found in the literature and proposes a new approach of ambulance deployment based upon Big Bang Big Crunch optimization algorithm. The applied approach was implemented and validated on MATLAB. Findings: This technique allows us to cover the entire state of Punjab which is divided into 8 sectors with the fleet of 236 ambulances providing a coverage radius of 8.5 kilometers by an individual ambulance. Improvements/Applications: The applied soft- computing technique surpasses the existing techniques

Reduced-order modeling of linear time invariant systems using big bang big crunch optimization and time moment matching method

In this paper, a new approach is proposed to approximate the high-order linear time invariant (LTI) system into its low-order model. The proposed approach is a mixed method of model order reduction scheme consisting of recently developed big bang big crunch optimization algorithm and the time-moment matching method. This proposed method is applicable to single-input single-output, multi-input multi-output system, and time delayed LTI systems. The proposed approach is substantiated with various numerical examples of low and high-order systems. The results show that the reduced-order models preserve both transient and steady state conditions of original systems. Further, the results are also compared with the existing approaches of reduced order modeling which show exceptional improvement in integral square error (ISE) and other time domain specifications.

A Hybrid Big Bang–Big Crunch optimization algorithm for solving the different economic load dispatch problems

In this paper, we applied a Hybrid Big Bang–Big Crunch optimization technique for solving the different types of economic load dispatch (ELD) problems in power systems. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zone, and non-smooth cost functions are considered using the proposed method in practical generator operation. The proposed method is tested on three different systems (six-unit system considering losses, 15 units: ED considering transmission loss, large system: 40 generating units with valve-point loading effects). Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this technique to highly nonlinear and different ELD problem.

Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm

The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO) is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA)-based, Biogeography-based Optimization (BBO)-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.

An Optimal Robust Tuning Method for First Order plus Dead Time Systems with Parameter Uncertainty

This paper presents a simple PI tuning method for first order plus dead time (FOPDT) systems. Firstly, stabilizing PI parameters region is found for these kinds of systems. Then, optimal point of this region is selected using the Big Bang Big Crunch optimization algorithm and this work is extended to FOPDT systems that contain parameter uncertainty. After the optimizations done for a wide range of systems, an optimal and robust PI tuning method is proposed with a curve fitting of the obtained optimization data. Lastly, the proposed method is compared with other PI tuning methods for their performance and robustness.

Congestion Management Using Real and Reactive Power Rescheduling Based on Big Bang-Big Crunch Optimization Algorithm

With appearance of deregulated electricity markets, line congestions in transmission systems is considered as critical problem for power system which may prevent dispatching all of the contracted power transactions. Many studies have been carried out to express techniques for congestion management (CM). In this paper real and reactive power rescheduling of generators approach is utilized. This method includes two steps. First one is optimum selection of generators on the basis of sensitivities of generator to power flow on congested line/lines. Next step is optimal rescheduling of generators and identification of capacitors required to provide real and reactive power support for CM. Optimization process in this paper is carried out using big bang-big crunch (BB-BC) algorithm which is improved by particle swarm optimization (PSO) method as hybrid BB-BC (HBB-BC) optimization for the first time. Effectiveness of proposed method has been tested on IEEE 30-bus system, the 39-bus New England system, and 75-bus Indian system and compared with the other proposed methodologies.

Environmental/economic power dispatch using a Hybrid Big Bang–Big Crunch optimization algorithm

The combined economic and emission dispatch (CEED) problem where objective function is highly non linear, non-differentiable and may have multiple local minima. Therefore, classical optimization methods may not converge or get trapped to any local minima. This paper proposes a Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm technique for solving the CEED. Six generator test and IEEE 30 standard bus system was used for testing and validation purposes. The preference of the HBB–BC is compared with other heuristic methods. The results show, clearly, that the proposed method gives better optimal solution as compared to the other methods.

Optimum design of steel frames with semi-rigid connections using Big Bang-Big Crunch method

The Big Bang-Big Crunch (BB-BC) optimization algorithm is developed for optimal design of non-linear steel frames with semi-rigid beam-to-column connections. The design algorithm obtains the minimum total cost which comprises total member plus connection costs by selecting suitable sections. Displacement and stress constraints together with the geometry constraints are imposed on the frame in the optimum design procedure. In addition, non-linear analyses considering the P- effects of beam-column members are performed during the optimization process. Three design examples with various types of connections are presented and the results show the efficiency of using semi-rigid connection models in comparing to rigid connections. The obtained optimum semi-rigid frames are more economical solutions and lead to more realistic predictions of response and strength of the structure.

Optimal design of structures with multiple natural frequency constraints using a hybridized BB-BC/Quasi-Newton algorithm

A hybridization of the Quasi-Newton with Big Bang-Big Crunch (QN-BBBC) optimization algorithm is proposed to find the optimal weight of the structures subjected to multiple natural frequency constraints. The algorithm is based on hybridizing a mathematical algorithm (quasi-Newton) for local search and a meta-heuristic algorithm (Big Bang-Big Crunch) for global search, and to help to leave the traps. Four examples are proposed for the optimization of trusses and two examples are studied for the optimization of frames with frequency constraints. The examples are widely reported and used in the related literature as benchmarks. The numerical results reveal the robustness and high performance of the suggested methods for the structural optimization with frequency constraints.

An Efficient Hybrid Big Bang–Big Crunch Algorithm for Multi-objective Reconfiguration of Balanced and Unbalanced Distribution Systems in Fuzzy Framework

This article presents an efficient hybrid big bang–big crunch optimization algorithm to solve the multi-objective reconfiguration of balanced and unbalanced distribution systems in a fuzzy framework. The objectives considered are the minimization of total real power losses, the minimization of buses voltage deviation, and load balancing in the feeders. First, each objective is fuzzified, and then the overall fuzzy satisfaction function formed is considered as a fitness function and maximized during the optimization process. The hybrid big bang–big crunch algorithm is an effective and powerful method that has high accuracy and fast convergence, and its implementation is easy. This algorithm using particle swarm optimization capacities improves the capability of the big bang–big crunch algorithm for better exploration. In addition, the hybrid big bang–big crunch uses a mutation operator after position updating to avoid local optimum and to explore new search areas. The effectiveness of the proposed algorithm is demonstrated on balanced and unbalanced test distribution systems. The simulation results are compared with the solutions obtained by other approaches.

CO2 and cost optimization of reinforced concrete frames using a big bang-big crunch algorithm

A hybrid Big Bang-Big Crunch (BB-BC) optimization algorithm is applied to the design of reinforced concrete frames. The objective of the optimization is to minimize the total cost or the CO2 emissions associated with construction of reinforced concrete frames subjected to constraints based on the specifications and guidelines prescribed by the American Concrete Institute (ACI 318-08).Designs are presented for several reinforced concrete frames that minimize the cost and the CO2 emissions associated with construction. In the first frame example, low-cost designs developed using BB-BC optimization are compared to designs developed using a genetic algorithm. In the second set of frame designs, both low-cost designs using BB-BC optimization are compared to designs developed using simulated annealing. The BB-BC algorithm generated designs that reduced the cost and the CO2 emissions of construction for example frames.