Big Bang-Big Crunch Algorithm Research Articles

Techno-economic analysis and optimal sizing of a battery-based and hydrogen-based standalone photovoltaic/wind hybrid system for rural electrification in Cameroon based on meta-heuristic techniques

In developing nations, particularly in African countries, where difficulties with rural electrification are highly severe, hybrid power systems appeared as a suitable method of distributing power to rural and off-grid locations. Cameroon, like other African countries, is facing a serious challenge of access to basic energy services for its population. The country's constraints present a global situation of less than 48% of the population having access to electrical energy. This situation is more frequent in rural areas. In this paper, a comparative assessment of meta-heuristics optimization techniques is conducted for optimal design of photovoltaic/wind/fuel cell and photovoltaic/wind/battery hybrid systems to constantly supply-three typical loads demand in Kousseri, Cameroun. For this purpose, four well-known meta-heuristics techniques namely artificial bee colony algorithm, cuckoo search algorithm, imperialist competitive algorithm and big bang-big crunch algorithm are implemented to solve the optimization problem, consisting of minimizing the net present cost while increasing the system reliability characterized by loss of power supply probability. Four others configurations namely photovoltaic/battery, wind/battery, photovoltaic/fuel cell and wind/fuel cell are also evaluated and the outcomes are compared with the results of the photovoltaic/wind/fuel cell and photovoltaic/wind/battery systems with regards to the life-cycle cost and cost of energy. To determine the most affordable configurations, simulations and optimizations studies are conducted. Our findings revealed that, the cuckoo search algorithm performs better than other algorithms. The results also show that, photovoltaic/wind/battery standalone hybrid system is the more reliable and cost-effective configuration for satisfying the three typical electrical loads demand encountered at Kousseri, Cameroon. The obtained optimal number of component and cost of energy of the photovoltaic/wind/battery hybrid systems are as follows: for heavy activity the optimal system integrates 27 PV panels, 1 wind turbine, 38 batteries banks, 1 inverter and the obtained cost of energy corresponds to 0.1959$/kWh; for medium activity, 13 PV panels, 1 wind turbine, 26 batteries banks and 1 inverter which resulted to a cost of energy of 0.2386$/kWh and for small activity, the best configuration includes 19 PV panels, 5 wind turbines, 18 batteries banks and 1 inverter with a cost of energy of 0.2641$/kWh.

Design of PIλ-PDμ controller for high-order systems based on model order reduction using BB-BC and time moment matching

In this paper, a practical fractional proportional–integral and proportional–derivative (PIλ-PDμ) controller tuning method and a model reduction method are proposed for high-order systems with or without time delay. First, the Big Bang–Big Crunch (BB-BC) algorithm and the time moment matching method are used to reduce the order of the high-order system function, and the approximate low-order system function is obtained. Second, the parameter region of fractional-order PIλ-PDμ controller is obtained according to the D partition method, and the improved weighted geometric center (WGC) method is introduced to select the special points in the stable region as the control parameters. Then, the fractional-order PIλ controller is discretized by Muir method, and the fractional-order PDμ controller is discretized by continued fraction expansion (CFE). Finally, the BB-BC algorithm is used to optimize the control parameters, and the parameters with better control performance are obtained. The simulation results show that the fractional PIλ-PDμ parameter setting method based on BB-BC algorithm can improve the control performance of the system, and the model reduction scheme based on the combination of moment matching method and BB-BC algorithm is effective in both time domain and frequency domain analysis.

A New Optimization Method for Gapped and Distributed Core Magnetics in LLC Converter

LLC converter design optimization remains a challenging task for varying loads such as in battery chargers. There are numerous L-L-C combinations to choose from a design space that can satisfy the required voltage gains of the application. An accurate magnetic model is essential to optimally size the passive components according to the application needs. This paper provides a new design tool for gapped core magnetics to optimize the transformer and resonant inductor in LLC converters. Unlike conventional design algorithms, the proposed algorithm considers multiple distributed cores and selects the optimal magnetic flux density by minimizing a penalty function that includes power loss, cost and volume of the magnetic components using Big-Bang Big-Crunch Algorithm. The gapped core transformer and inductor design equations have been verified in Ansys Maxwell and Simplorer co-simulation environment for a 3700W 48V LLC and calculated power loss have been compared with experimental results. For the given L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> pair of 37.52 μH and 9.38 μH, the proposed magnetic model designed a transformer with two-distributed cores, each one exhibiting a magnetizing inductance of 17.73 μH and leakage inductance of 0.7 μH on a EE422120 with 3F36 material. The total power loss of the transformers are measured as 12.44 W on a 3700 W prototype switched at 350 kHz.

Fractional PID controller based on biggest log modulus tuning method with MOPSO optimization for distillation column

The main contribution of this work is to design a fractional order proportional integral derivative (FO-PID) controller by combining the biggest log modulus tuning (BLT) method and multi-objective particle swarm optimization (PSO) algorithm for the control of the challenging multivariable systems. The parameters of the integer proportional integral (PI) controller are designed preliminary using BLT method. The derivation parameter, the fractional integrator and the fractional derivation parameters is formulated as an optimization problem with many objective functions as minimizing the integral square error (ISE), integral time absolute error (ITAE) and objective function which contain the ISE, overshoot and settling time using PSO algorithm. An example of wood and berry distillation column is treated in this paper. A comparison between integer BLT, integer PSO, big bang-big crunch (BB-BC) algorithm, TLBO method and the proposed fractional BLT-PSO method is carried out. The simulation results using MATLAB/Simulink show the efficiency and merits of the proposed method for such systems.

Chronic obstructive pulmonary disease prediction using Internet of things-spiro system and fuzzy-based quantum neural network classifier

Chronic Obstructive Pulmonary Disease (COPD) is a multifarious progressive disease that increases the mortality and morbidity ratio as well as becoming a life-threatening issue of an individual. Accurate and cost-effective diagnosis of diseases plays a primary role in the medical domain and a wide range of research has been carried out on disease prediction using sensory approaches along with the assistance of machine learning techniques. The traditional disease diagnosis procedures are invasive, costlier and the decision support systems were unreliable most of the time. The human exhaled breath discharged from the body is composed of various Volatile Organic Compounds (VOCs) which can be influenced by metabolic and disease activities. Hence, the analysis of VOCs in exhaled breath has an incredible potentiality for COPD diagnosis and can rapidly decrease the mortality rate. In this research, IoT-Spiro System is designed and an intelligent machine learning forecasting framework (IMLFF) has been proposed. IoT-Spiro System perceives the various VOCs patterns available in exhaled breath and that real-time parameter has been analyzed using IMLFF. The proposed framework incorporates a hybrid Genetic Big Bang-Big Crunch (GBB-BC) algorithm for selecting the optimal features from the real-time dataset and a Fuzzy-based Quantum Neural Network (F-QNN) classifier for diagnosing COPD. The experimental results illustrate that IMLFF outperforms when compared to recent existing approaches concerning various statistical parameters and performance metrics. From the result analysis, it has been determined that IoT-Spiro System and IMLFF framework can serve as an efficient assisting model to the medical practitioner for diagnosing COPD.

Niger Seed Oil-Based Biodiesel Production Using Transesterification Process: Experimental Investigation and Optimization for Higher Biodiesel Yield Using Box–Behnken Design and Artificial Intelligence Tools

The present work aims at cost-effective approaches for biodiesel conversion from niger seed (NS) oil by employing the transesterification process, Box–Behnken design (BBD), and artificial intelligence (AI) tools. The performances of biodiesel yield are reliant on transesterification variables (methanol-to-oil molar ratio M:O, reaction time Rt, catalyst concentration CC, and reaction temperature RT). BBD matrices representing the transesterification parameters were utilized for experiment reductions, analyzing factor (individual and interaction) effects, deriving empirical equations, and evaluating prediction accuracy. M:O showed a dominant effect, followed by CC, Rt, and RT, respectively. All two-factor interaction effects are significant, excluding the two interactions (Rt with RT and M:O with RT). The model showed a good correlation or regression coefficient with a value equal to 0.9869. Furthermore, the model produced the best fit, corresponding to the experimental and predicted yield of biodiesel. Three AI algorithms were applied (the big-bang big-crunch algorithm (BB-BC), firefly algorithm (FA), and grey wolf optimization (GWO)) to search for the best transesterification conditions that could maximize biodiesel yield. GWO and FA produced better fitness (biodiesel yield) values compared to BB-BC. GWO and FA experimental conditions resulted in a maximum biodiesel yield equal to 95.3 ± 0.5%. The computation time incurred in optimizing the biodiesel yield was found to be equal to 0.8 s for BB-BC, 1.66 s for GWO, and 15.06 s for FA. GWO determined that the optimized condition is recommended for better solution accuracy with a slight compromise in computation time. The physicochemical properties of the biodiesel yield were tested according to ASTM D6751-15C; the results are in good agreement and the biodiesel yield would be appropriate to use in diesel engines.

Hybrid Big Bang-Big Crunch based resource scheduling to improve QoS in cloud infrastructure

The service-oriented computing paradigm changes the way of computing. Emerging technologies like grid computing, cloud computing, and smart health care application have changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to the requirement for real-time applications. Cloud computing comes with pay per use model to pay for only those resources that you have used. Inside the cloud there lie many issues related to efficient and cost-effective models to improve cloud performance and complete the client task with the least cost and high performance. E-Health care services are one of the most computational intensive services in the cloud, they require real-time computing which can only be achieved if the computational resources can compute it in the least time. Cloud can accomplish this using an efficient scheduling algorithm. This manuscript focuses on the task scheduling technique which enhances the performance in real-time with the least execution time, network cost, and execution cost. The presented model is inspired by Big Bang-Big Crunch algorithm in astronomy. The presented algorithm enhances the quality of service by reducing the scheduling delay, network delay with the least resource cost to complete the task in the least cost to the user with high quality of service.

Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer

In this study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains a diesel generator, photovoltaics (PV), a wind turbine (WT), and batteries as a storage system. The proposed HRES aims to electrify a remote region in northern Saudi Arabia based on annualized system cost (ASC) minimization and power system reliability enhancement. To differentiate and evaluate the performance, the BO was compared to four recent metaheuristic algorithms, called big-bang–big-crunch (BBBC), crow search (CS), the genetic algorithm (GA), and the butterfly optimization algorithm (BOA), to find the optimal design for the proposed off-grid HRES in terms of optimal and worst solutions captured, mean, convergence rate, and standard deviation. The obtained results reveal the efficacy of BO compared to the other four metaheuristic algorithms where it achieved the optimal solution of the proposed off-grid HRES with the lowest ASC (USD 149,977.2), quick convergence time, and fewer oscillations, followed by BOA (USD 150,236.4). Both the BBBC and GA algorithms failed to capture the global solution and had high convergence time. In addition, they had high standard deviation, which revealed that their solutions were more dispersed with obvious oscillations. These simulation results proved the supremacy of BO in comparison to the other four metaheuristic algorithms.

Guest Editorial: Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications

These days, hybrid nature-inspired population-based intelligent optimization methods are a wide range of the algorithms, which are used very often to solve real-world (industrial) optimizationproblems. As it was shown in [item 1) in the Appendix] by Slowik and Cpalka, nature-inspired methods can be divided into several groups of algorithms. In these groups of nature-inspired algorithms, we can find physics-based algorithms (gravitational search algorithm, harmony search algorithm, big bang-big crunch algorithm, etc.) and bio-inspired methods. In bio-inspired methods, we can find evolutionary algorithms (genetic algorithms, evolution strategies, genetic programming, etc.), swarm intelligence algorithms (particle swarm optimization algorithm, ant colony optimization algorithm, bat algorithm, etc.), immune algorithms (clonal selection algorithm, negative selection algorithm, etc.), and others (flower pollination algorithm, great Salomon run, Japanese tree frogs calling, etc.). These four selected groups of nature-inspired population-based optimization algorithm are commonly used in creating hybrid methods.

An optimization technique for battery energy storage with wind turbine generator integration in unbalanced radial distribution network

In this paper, an optimal technique for optimal location of battery energy storage system (BESS) and capacity wind power penetrations charging/discharging BESS dispatch is proposed. In this proposed work, the Big Bang-Big Crunch (BB-BC) algorithm is proposed and performance of BB-BC must be upgraded along chaotic initialization and chaotic search for acquiring that optimal switching pulse and henceforth it is named as CBCA technique. CBCA can improve the efficiency of unbalanced distribution network under technical limitations. The proposed method utilizes the average power loads of decisive criterion to charge or discharge the battery. Wind power generation is used entirely for charging the battery. Here, free-running and conservative battery discharge techniques are explored. The proposed method runs on IEEE 37 bus unbalanced radial distribution network (URDN) system. The simulation outcomes demonstrate that proposed method improves several efficiency objectives in the distribution system through optimally coordinating BESS shipments.

Artificial neural network integrated with bio-inspired approach for optimal VAr management and voltage profile enhancement in grid system

ABSTRACT This article presents the design of an improved smart grid model with an improved optimal VAr strategy using a bio-inspired approach. The integration of an artificial neural network technique with bio-inspired algorithms is the novelty of this research work. The objective of the work is attained by integrating the artificial neural network with three different evolving techniques like the Big Bang-Big Crunch algorithm, the gravitational search algorithm, and bacterial foraging algorithm. The three different layers of artificial neural network reconcile with the power grid for enhancing the voltage profile generated by bio-inspired techniques. The effectiveness and reliability of the algorithms are examined on the standard IEEE 30 bus system. The sample test bus system comprises six Generators, four Transformer tap changers, and 2 Static Shunt VAr Compensator. Artificial intelligence technology is applied for enhancing the voltage profile by managing reactive power across the power system network. The results produced by artificial intelligence technique are more competent and outstanding than any other conservative optimization techniques. It is found that the real power loss across the network has been reduced from 5.744 MW to 4.234 MW, while the bus voltage deviation has been minimized from 1.475 p.u. to 0.0918 p.u. and the voltage profile has been improved from 0.968 to 1.0985 p.u. after integration of the artificial neural network. The optimal values of objective functions are attained by evaluating the training and testing performance of artificial neural network criteria such as coefficient of determination, mean absolute error, root mean square error, and mean absolute percentage error. The obtained results show the remarkable improvement in voltage profile after integrating artificial intelligence technology with a power grid system.

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.

Chaotic-Based Big Bang-Big Crunch Algorithm (CBCA) for Energy Management System of Hybrid Isolated Micro-grid: An Inherent Control Scheme

ABSTRACT In this manuscript, an energy management and maximum power point tracking (MPPT) to micro-grid based on hybrid renewable energy sources (HRESs) like wind, solar, battery using an inherent control scheme. The proposed control scheme is the chaotic-based big bang-big crunch algorithm (CBCA). Here, chaotic-based techniques have been integrated into BB-BC to get the global optimum solution. In the proposed control scheme, the CBCA streamlines the parameter of the sources converter according to the required load demand and creates the optimal switching pulses. By the proposed control scheme the sources are controlled and operate in the MPPT during low energy generation or off-MPPT modes during the excess of energy to meet the load requirement based on the system power balance and energy constraints. The proposed control scheme is designed to operate the micro-grid as an autonomous system considering the power obtainable from renewable sources, the load power requirement including the condition of the battery. Finally, the voltage on the load side is controlled to assure proficient power transfer from the source of energy to the load. With this proper control, the proposed technique presents a steady control function at every micro-grid subsystems subject to different power generation including load conditions. By then, the performance of the CBCA scheme is implemented in the MATLAB/Simulink work site and also the implementation is likened to the existing approaches like GOA and AGONN. The statistical analysis of CBCA with existing approaches under cases 1, 2, and 3 has been analyzed. The CBCA method under cases 1, 2, and 3 of mean, median, the standard deviation is more efficient than the existing solution strategies. The mean value of the CBCA method under case 1 indicates 1.2641. The median value of the CBCA method under case 2 implicates 1.2204. The standard deviation value of the CBCA method under case 3 denotes 0.0762.

A comparison of combat genetic and big bang–big crunch algorithms for solving the buffer allocation problem

The buffer allocation problem (BAP) aims to determine the optimal buffer configuration for a production line under the predefined constraints. The BAP is an NP-hard combinatorial optimization problem and the solution space exponentially grows as the problem size increases. Therefore, problem specific heuristic or meta-heuristic search algorithms are widely used to solve the BAP. In this study two population-based search algorithms; i.e. Combat Genetic Algorithm (CGA) and Big Bang-Big Crunch (BB-BC) algorithm, are proposed in solving the BAP to maximize the throughput of the line under the total buffer size constraint for unreliable production lines. Performances of the proposed algorithms are tested on existing benchmark problems taken from the literature. The experimental results showed that the proposed BB–BC algorithm yielded better results than the proposed CGA as well as other algorithms reported in the literature.

Structural Optimization of Ship Lock Heads during Construction Period considering Concrete Creep

Traditional structural optimization is mainly based on the assumption that the materials are elastic, which cannot represent real stress fields in structures. In this study, the genetic algorithm, big bang-big crunch algorithm, and hybrid big bang-big crunch algorithm were employed to optimize the design factors of ship lock heads during concrete construction. The optimization goal was to determine the minimum volume of concrete. The factors considered included the hydration heat, the early-stage creep, and the transient deformation under external loads. In the finite element analysis, three types of boundary conditions were considered. The whole construction process was simulated, and the maximum tensile and compressive stresses, the stability, and the overturning of the lock head were examined. Based on the finite element analysis, to reduce the consumption of memory, a set of implicit recursive equations were used to calculate the thermal creep stress. Thirty-four design variables were distinguished for optimization. A case study on the optimization of a ship lock head was used to demonstrate the optimization process. The optimization results showed that the hybrid big bang-big crunch algorithm was more effective, and some conclusions were derived.

Design optimization of real-size steel frames using monitored convergence curve

It is an undeniable fact that there are main challenges in the use of metaheuristics for optimal design of real-size steel frames in practice. In general, steel frame optimization problems usually require an inordinate amount of processing time where the main portion of computational effort is devoted to myriad structural response computations during the optimization iterations. Moreover, the inherent complexity of steel frame optimization problems may result in poor performance of even contemporary or advanced metaheuristics. Beside the challenging nature of such problems, significant difference in geometrical properties of two adjacent steel sections in a list of available profiles can also mislead the optimization algorithm and may result in trapping the algorithm in a poor local optimum. Consequently, akin to other challenging engineering optimization instances, significant fluctuations could be observed in the final results of steel frame optimization problems over multiple runs even using contemporary metaheuristics. Accordingly, the main focus of this study is to improve the solution quality as well as the stability of results in metaheuristic optimization of real-size steel frames using a recently developed framework so-called monitored convergence curve (MCC). Two enhanced variants of the well-known big bang-big crunch algorithm are adopted as typical contemporary metaheuristic algorithms to evaluate the usefulness of the MCC framework in steel frame optimization problems. The numerical experiments using challenging test examples of real-size steel frames confirm the efficiency of the MCC integrated metaheuristics versus their standard counterparts.

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.

Accurate indoor positioning with ultra-wide band sensors

Ultra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m × 5.41 m and 50 cm × 50 cm grids has been selected, and a total of 27,000 measurements have been collected from 180 test points. The average positioning error of this test bed is calculated as 16.34 cm. Then, several combinations of algorithms are applied to raw data. The combination of Big Bang-Big Crunch algorithm for optimization, and then the Kalman Filter have yielded the most accurate results. Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm.

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.