Big Bang-Big Crunch Research Articles

Optimal design of planar steel frame structures utilizing meta-heuristic optimization algorithms

Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, seven population-based meta-heuristic algorithms are employed for size optimization of two-dimensional steel frame structures. These algorithms consist of the Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Cyclical Parthenogenesis Algorithm, Cuckoo Search algorithm, Thermal Exchange Optimization algorithm, Teaching-Learning-Based Optimization algorithm, and Water Evaporation Optimization algorithm. Optimization aims to minimize the weight of rigid-jointed steel frame structures while satisfying some constraints on displacement and stress limits. The design is based on the requirements of the AISC Load and Resistance Factor Design (LRFD). The capability and robustness of the algorithms are investigated through three well-known steel frame benchmarks.

An FPGA-Based Accelerated Optimization Algorithm for Real-Time Applications

Many modern applications, such as industrial control, image processing and machine learning, are based on optimization algorithms that must solve compute-intensive problems under real-time constraints taking into account real-world parameters that evolve in time. Among others, evolutionary algorithms (EAs) are increasingly used in real-time applications to solve such complex optimization problems. Moreover, Field Programmable Gate Arrays (FPGAs) have been proved an effective platform for the implementation of these algorithms satisfying real-time and low-power requirements. In this paper, we study the FPGA-based acceleration of the Big Bang-Big Crunch (BB-BC) algorithm. BB-BC is an optimization method inspired by the corresponding evolutionary theory of the universe [1]. The BB-BC method is performed in two phases: in the Bing Bang phase, similarly to other Genetic Algorithms (GAs) it generates a random population of candidate solutions, while in the Big Crunch phase it shrinks these candidates around an optimal point. It has been shown that the BB-BC method outperforms classical GA algorithms for several optimization problems in terms of convergence speed. We show that the BB-BC algorithm does not suffer from the design limitations of the classical GAs that impede the performance of their hardware-based accelerators. We propose an efficient fully pipelined design of both BB-BC phases and a parallel scheme which integrates several BB-BC pipelined engines to improve system performance. We implement the proposed FPGA-based accelerator on a Xilinx Virtex-5 development board for three different fitness functions and compare the execution time against its software counterpart (in C language) and a CUDA program running on a massively parallel computing platform (GPU). We also compare the proposed optimized FPGA architecture with an RTL design generated by a high-level synthesis (HLS) design flow. We propose an Adaptive Neuro-fuzzy Inference System (ANFIS) model for fitness function approximation method to reduce the execution latency of complex fitness functions. We also demonstrate the efficiency of the proposed FPGA architecture on an image search problem, finding the darkest pixel of a grey image. The experimental results show that the proposed approach achieves significant speedup compared to the other software versions of the BB-BC algorithm and converges much faster than a typical GA algorithm making it an ideal solution for real-time embedded applications.

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.

PID controller optimized by Big Bang-Big Crunch algorithm for the evolution of router bandwidth demand

This paper presents an application of the Big Bang-Big Crunch algorithm to estimate with high precision the gains Kp, Ki and Kd of a PID controller. The optimized PID controller together with an evolution model is useful for the network administrators as it provides information within 5 or 15-min intervals for the bandwidth distribution. Furthermore, the Big Bang-Big Crunch algorithm results will be compared with those computed by the analysis of a classical PID controller. Finally, simulations with Simulink package are presented to compare the two methodologies.

Optimal Design of Reinforced Concrete Cantilever Retaining Walls Utilizing Eleven Meta-Heuristic Algorithms: A Comparative Study

In this paper, optimum design of reinforced concrete cantilever retaining walls is performed under static and dynamic loading conditions utilizing eleven population-based meta-heuristic algorithms. These algorithms consist of Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Teaching-Learning-Based Optimization algorithm, Imperialist Competitive Algorithm, Cuckoo Search algorithm, Charged System Search algorithm, Ray Optimization algorithm, Tug of War Optimization algorithm, Water Evaporation Optimization algorithm, Vibrating Particles System algorithm, and Cyclical Parthenogenesis Algorithm. Two well-known methods consisting of the Rankine and Coulomb methods are used to determine lateral earth pressures acting on cantilever retaining wall under static loading condition. In addition, Mononobe-Okabe method is employed for dynamic loading condition. The design is based on ACI 318-05 and the goal of optimization is to minimize the cost function of the cantilever retaining wall. The performance of the utilized algorithms is investigated through an optimization example of cantilever retaining wall. In addition, convergence histories of the algorithms are provided for better understanding of their performance.

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.

B-Spline Curve Approximation by Utilizing Big Bang-Big Crunch Method

B-Spline Curve Approximation by Utilizing Big Bang-Big Crunch Method

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.

A Comparative Study of Nature-Inspired Metaheuristic Algorithms in Search of Near-to-optimal Golomb Rulers for the FWM Crosstalk Elimination in WDM Systems

ABSTRACTNowadays, nature-inspired metaheuristic algorithms are the most powerful optimizing algorithms for solving NP-complete problems. This paper proposes five recent approaches to find near-optimal Golomb ruler (OGR) sequences based on nature-inspired algorithms in a reasonable time. The optimal Golomb ruler sequences found their application in channel-allocation method that allows suppression of the crosstalk due to four-wave mixing (FWM) in optical wavelength division multiplexing (WDM) systems. The simulation results conclude that the proposed nature-inspired metaheuristic optimization algorithms are superior to the existing conventional computing algorithms, i.e., Extended Quadratic Congruence (EQC) and Search algorithm (SA) and nature-inspired algorithms, i.e., Genetic algorithms (GAs), Biogeography-based optimization (BBO) and simple Big bang–Big crunch (BB-BC) optimization algorithm to find near-OGRs in terms of ruler length, total optical channel bandwidth and computation time.

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.

HGAB3C: A new hybrid global optimization algorithm

This paper proposes a new optimization algorithm, namely HGAB3C, and presents its performance on the CEC-2014 test suite. In HGAB3C, simple genetic algorithms (GAs) and big bang-big crunch (BB-BC) are hybridized. The algorithm carries out global searches using a simple GA. In every generation the BB-BC algorithm is used to carry out local searches. The addition of local search has improved the capability of simple GAs significantly. The performance of the proposed algorithm is compared with 17 other optimization algorithms on all 30 functions of the CEC-2014 benchmark suite. It is observed that HGAB3C outperforms all other algorithms on 4 benchmark functions. For the 3 other functions, its performance equaled the best of the competing algorithms, which makes HGAB3C's performance best in a total of 7 benchmark functions. Out of the 18 competing algorithms, the proposed algorithm ranked second for the unmatched best mean error measure. For the best performance measure (number of functions giving unmatched best and equaled best mean error), the proposed algorithm was the third best. As far as the speed of convergence is concerned, the algorithm gave an unmatched best performance for the shifted Schwefel function (function 10 of CEC-2014 test bench). It obtained a mean error value of 0.00E+00, outperforming the previous best of 1.23E-03, converging to the target result in an average of 346.44 generations, which no other algorithm could achieve.

ANN System Identification for Rapid Battery Charger using Parallel 3 Parent Genetic Algorithm

Model identification is one of the main concerns in the field of system modeling. The complete modeling of an ANN system using input output data consists of two processes: architecture selection in which number of hidden layers and the number of neurons in each hidden layer is to be decided. This is then followed by training of system by the given training data. The problem here is formulated as search and minimization problem. This paper presents the identification of ANN system for rapid Nickel Cadmium (Ni-Cd) batteries charger by applying a new version of Genetic algorithm called as parallel 3 parent genetic algorithm (P3PGA). It is a multi population based 3 parent genetic algorithm (3PGA) .The proposed approach was implemented using MATLAB R2018A and was observed to be computationally more efficient with minimum MSE. With increase in number of iterations, system performance gets improved. We further compared results of the proposed algorithm with the results of other recent soft computing based algorithms as well classical learning based algorithms namely, big bang big crunch (BB-BC), parallel big bang big crunch (PBB-BC), Levenberg-Marquardt algorithm (LM), error back propagation (EBP), Resilent prop (RPROP), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) for ANN model identification. The proposed algorithm outperformed all of the other 8 algorithms.

Model Order Reduction of Commensurate Fractional-Order Systems Using Big Bang – Big Crunch Algorithm

ABSTRACT In this paper, a soft computing based scheme is proposed for the model order reduction of single input-single output commensurate fractional-order (FO) systems. The fractional-order system is first converted into an integer order (IO) system. The reduced order model of the corresponding integer order system is then determined via big bang – big crunch (BBBC) optimization algorithm. Finally, the reduced IO transfer function is transferred back to its FO form through an inverse substitution. The proposed approach is substantiated by two numerical examples of different orders. The effectiveness of the technique is demonstrated by the comparison of two performance indices, i.e. integral square error (ISE) and error and the response characteristics in time and frequency domain with the existing techniques from the literature. It is revealed that the response of the proposed BBBC based reduced order model is much closer to that of the original higher-order system.

Inverse and direct optimization shape of airfoil using hybrid algorithm Big Bang-Big Crunch and Particle Swarm Optimization

Inverse and direct optimization shape of airfoil using hybrid algorithm Big Bang-Big Crunch and Particle Swarm Optimization

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization.

This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

Cellular Automata Based Model for E-Healthcare Data Analysis

E-healthcare is warm area of research and a number of algorithms have been applied to classify healthcare data. In the healthcare field, a large amount of clinical data is generated through MRI, CT scans, and other diagnostic tools. Healthcare analytics are used to analyze the clinical data of patient records, disease diagnosis, cost, hospital management, etc. Analytical techniques and data visualization are used to get the real time information. Further, this information can be used for decision making. Also, this information is useful for the better treatment of patients. In this work, an improved big bang-big crunch (BB-BC) based clustering algorithm is applied to analyze healthcare data. Cluster analysis is an important task in the field of data analysis and can be used to understand the organization of data. In this work, two healthcare datasets, CMC and cancer, are used and the proposed algorithm obtains better results when compared to MEBB-BC, BB-BC, GA, PSO and K-means algorithms. The performance of the improved BB-BC algorithm is also examined against benchmark clustering datasets. The simulation results showed that proposed algorithm improves the clustering results significantly when compared to other algorithms.

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.

Optimum design of tied-arch bridges under code requirements using enhanced artificial bee colony algorithm

In this study, optimum design algorithm is presented for tied-arch bridges under AASHTO-LRFD Bridge Design Specifications provisions. It is decided that in tied-arch bridges ties, arch ribs, and bottom and top bracings are made of built-up box sections, whereas built-up I sections are utilized for floor beams and stringers. Bars are adopted for hangers. In the formulation of the optimization problem, design variables are selected as the cross sectional dimensions of steel plates not that of I and box sections. Design pools are prepared for steel plate sections in addition to the hanger bars so that the optimization algorithm can select appropriate steel plates, construct I and box built-up sections for members of 3-D tied-arch such that the weight of the bridge is minimized. In addition to design code requirements, geometrical constraints among its elements that are required for manufacturability of the bridge are also considered. The design process of tied-arch bridges differs from that of steel framed structures. It necessitates consideration of moving vehicle loads. Design optimization algorithms require the response of bridges under several design load arrangements and in the construction of influence lines. This is achieved by using open application programming interface (OAPI) facility of SAP2000. The solution of discrete nonlinear programming problem is obtained by using the proposed Enhanced Artificial Bee Colony algorithm (eABC). The proposed algorithm is compared with Standard Artificial Bee Colony (ABC) and Exponential Big Bang-Big Crunch (eBB-BC) algorithms to evaluate its performance.

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.

Hydrothermal Economic Dispatch Using Hybrid Big Bang-Big Crunch (HBB-BC) Algorithm

In power generation, it is expected to generate required amount of power based on the load system with minimum cost which is generally called as economic dispatch (ED). Therefore, providing an approach which can be used to solve ED problem in electric systems for effective operation is important. In this paper, hybrid big bang-big crunch (HBB-BC) algorithm was proposed to solve hydrothermal ED problem. Basically, HBB-BC is an algorithm which combined between BB-BC and PSO that use the PSO to improve ability of BB-BC algorithm. To examine the performance of HBB-BC algorithm for hydrothermal ED, IEEE 26 bus was utilized as test system with 6 generators (3 thermal and 3 hydro generator units). Next, simulation output was compared with improved PSO, and modified improved PSO algorithm. From results, HBB-BC algorithm shown the best result under tested case. It confirmed by obtained total operating cost for HBB-BC algorithm was the lowest (11,344.593 $/h) compared to IPSO (11,451.001 $/h) and MIPSO algorithms (11,387.000 $/h).