Adaptive Firefly Algorithm Research Articles

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Introduction: The advantages and wide-ranging applications of cloud computing have made it a prominent attention for scholars these days. The dispersed structure of cloud and its whole dependence on the internet for service delivery extant security problems. Methods: Hypervisor attack detection is carried out in this study using Modified Advanced Encryption Standard (MAES) system which ensures security prominently. It finds and detects the attacks earlier for secured VM migration. The proposed system includes main phases including system framework, load balancing, resource allocation and hypervisor attack detection via MAES algorithm. Initially, Over the duration of cloud computing, consider the quantity of tasks, VM, and cloud users. In this research, the MMH system is employed for load balancing to balance the total burden across the cloud. Tasks are moved from overloaded to underloaded nodes to attain load balancing. Next, the allocation of resources is carried out utilizing Adaptive Firefly (AF) optimization system which is used to select best resources optimally. It generates the best fitness values to choose the best resources. Results: It is also focused to improve the cost metric, computational complexity, throughput and VM performance in cloud. Then, to detect hypervisor attacks, the MAES method is employed. It specializes on offering enhanced security for cloud data and is employed to identify hypervisor and VM attackers. Conclusion: The findings produced the conclusion that the suggested MAES method superior to the current approaches according to throughput, computation cost, Mean Square Error (MSE) rate, and energy use.

Characterization and optimization of 3D-printed, flexible vibration strain sensors with triply periodic minimal surfaces

Flexible piezoresistive strain sensors have promising applications in wearables and soft robotics. For sensing dynamic strains, such as a runner’s gait or a slipping object held by a robotic gripper, the sensor must be capable of vibration strain sensing for a range of amplitudes and frequencies. This article presents the characterization and design optimization of a flexible sensor using Schwarz P-type triply periodic minimal surface (TPMS) structures for vibration strain sensing. Sensors are fabricated using a 3D-printing process, coating a silicone rubber (SR) matrix with graphene nanoplatelets (GNP). Sensors are characterized under uniaxial compressive strain amplitudes from 0% to 10% and frequencies of 10–110 Hz. Frequency and time-domain analyses demonstrate sensor performance and explain the unique deformation mechanisms of the TPMS structure. Low sensor delays of less than 6.3 ms demonstrate its capability for high-frequency sensing. The frequency independence of the sensor is demonstrated, as the mean frequency dependence error is only ±3.89%. In addition, the TPMS sensor was parametrized, and a size optimization was performed on it using a multi-objective adaptive firefly algorithm (MOAFA). The MOAFA converged within 1618 unique function evaluations, a reduction of 85.5% compared to the entire set of feasible solutions. The optimized sensor was fabricated and characterized, displaying a reduced mean frequency dependence error of only ±2.18%.

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Proportional Integral Derivative (PID) control is the main control method in the process of agricultural water and fertilizer regulation, and its parameter setting directly affects the control effect of water and fertilizer regulation. However, the traditional PID parameters are adjusted manually such as using the critical proportionality method, which is time-consuming and difficult to achieve optimal control effects. To solve the optimal combination of PID control parameters and improve the control effect of water and fertilizer regulation, a partial attraction adaptive firefly algorithm (PAAFA) is proposed in this paper. Specifically, a partial attraction strategy is designed to speed up the convergence of the PAAFA and reduce the oscillation problem at the late stage of the algorithm. In addition, an adaptive inertia weight operator is proposed to balance the global search capability and local search capability of PAAFA and avoid the algorithm from trapping in the local optimum. Subsequently, to test the performance of PAAFA, the algorithm is subjected to a series of simulation experiments and bench tests with the latest methods, i.e., genetic algorithm (GA), Adaptive genetic algorithm (AGA), and firefly algorithm (FA) applied to PID parameter optimization problems. The simulation results demonstrate that the regulation times of the response curve of PAAFA-based PID control are reduced by 22.75%,10.10%and 20.61%, respectively, compared with GA, AGA, and FA. The bench test results show that the PAAFA-based PID control has the smallest relative error, and best control accuracy compared to GA, AGA, and FA, with an average relative error reduction of 3.99, 2.42, and 3.50 percentage points respectively.

A Steganography Embedding Method Based on CDF-DWT Technique for Reversible Data Hiding Application Using Elgamal Algorithm

Steganography is a data hiding method mainly used in the security purposes. While hiding more data in the embedding process the data may be lost and also cause some security problems. To avoid this problem a Steganography Embedding method is used. In this manuscript, Steganography Embedding method based on Cohen–Daubechies–Feauveau Discrete Wavelet Transform (CDF-DWT) technique to data hiding application using Elgamal algorithm is proposed. In this the cover image is taken for hiding the secret data. Then the cover image edges are detected and filtered with Speeded-Up Robust Features (SURF) method. Then the input secret data is encrypted with Elgamal algorithm. Then the secret data is hided under cover image for obtaining the stego image by process of Embedding using CDF DWT technique. In this data in the stego images are unreadable. To get readable secured data is extracted from the stego image and the data’s are decrypted to get secured secret data. The objective of this method is to safe guard the secret using Steganography method and to increase embedding efficiency, Embedding capacity and carrier capacity and to reduce the execution time. The MATLAB simulation results of the proposed CDF DWT technique with Elgamal algorithm portrays better outcomes such as Peak to Signal Noise Ratio (PSNR), Mean Square Error (MSE) (lower), Bit Error Rate (BER) was Lower, Execution time provides is lower, Carrier Capacity and Embedding Capacity are much higher and the values are compared with the existing method such as Elliptic Galois in cryptography and in the Steganography method with Adaptive Firefly Algorithm (EGC-AFA), Reversible Data hiding within Encrypted images Via Adaptive Embedding Strategy with block selection (RDHEI-AES), Double Linear Regression Prediction based Reversible Data Hiding in Encrypted Image (DLRP-RDHEI) respectively.

An adaptive firefly algorithm for multilevel image thresholding based on minimum cross-entropy

An adaptive firefly algorithm for multilevel image thresholding based on minimum cross-entropy

System cost minimisation in hybrid energy storage systems connected to microgrids: A comparative approach

Electricity is the most critical facility for humans. All traditional energy supplies are rapidly depleting. As a result, the energy resources are moved from traditional to non-conventional. In this research, mixture of two energy tools, namely wind and solar energy are used. Using a Hybrid Energy Storage System (HESS), continuous power can be provided. Electricity can be produced at a cost that is affordable. The integration of solar and wind in a hybrid system cause an increase in the system’s stability, which is the key benefit of this research. The system’s power transmission efficiency and reliability can be greatly enhanced by integrating these two intermittent sources. When one of the energy source is unavailable or inadequate to meet load demands, the other energy source will supply the power. The major contribution in this research is that, the proposed bidirectional single-inductor multiple-port (BSIMP) converter significantly lowers the component count, smaller circuit size and lower cost, allowing HESS to be integrated into DC microgrid. Minimum number of components are used for the same number of ESs in HESS in the proposed BSIMP converter. The hybridization of battery and supercapacitor (SC) for storage purpose is more cost effective, as compared to the battery energy storage system, thus improving the battery stress and hence used for large scale grid energy storage. SC’s are accepted as backup and found very useful in delivering high power, not possible with batteries. The use of SC in addition to batteries can be one solution for achieving the low life cycle economy. The Single Objective Adaptive Firefly Algorithm (SOAFA) is introduced for optimising the Proportional-Integral (PI) controller parameters. The system cost is reduced by about 32%, with the constraints on wind turbine swept area, PV area, total battery and SC capacity with the proposed optimisation algorithm.

Cloud resource management using adaptive firefly algorithm and artificial neural network

Cloud computing can be characterised in basic terms as one of the stages for giving conveyed figuring assets. Registering assets may incorporate capacity, data transfer capacity, memory space, handling components, etc. These assets are leased to the clients utilising the compensation per-utilised model. The interest for the assets may not be static, yet they can be mentioned on-request because of the developing web office. The clients of the cloud want to have a limited reaction time and cost yet then again, the cloud supplier centres on accomplishing proficient cloud assets distribution and limiting the support costs. Resource management is a practice of provisioning and managing the cloud resources efficiently. It also provides the techniques to provision the resources, schedule the jobs, and balance the loads. This proposal provides a resource management technique for efficient provisioning of resources and the scheduling of jobs on the static and dynamic cloudlet requests.

A Dynamic Adaptive Firefly Algorithm for Flexible Job Shop Scheduling

An NP-hard problem like Flexible Job Shop Scheduling (FJSP) tends to be more complex and requires more computational effort to optimize the objectives with contradictory measures. This paper aims to address the FJSP problem with combined and contradictory objectives, like minimization of make-span, maximum workload, and total workload. This paper proposes ‘Hybrid Adaptive Firefly Algorithm’ (HAdFA), a new enhanced version of the classic Firefly Algorithm (FA) embedded with adaptive parameters to optimize the multi objectives concurrently. The proposed algorithm has adopted two adaptive strategies, i.e., an adaptive randomization parameter (α) and an effective heterogeneous update rule for fireflies. The adaptations proposed by this paper can help the optimization process to strike a balance between diversification and intensification. Further, an enhanced local search algorithm, Simulated Annealing (SA), is hybridized with Adaptive FA to explore the local solution space more efficiently. This paper has also attempted to solve FJSP by a rarely used integrated approach where assignment and sequencing are done simultaneously. Empirical simulations on benchmark instances demonstrate the efficacy of our proposed algorithms, thus providing a competitive edge over other nature-inspired algorithms to solve FJSP.

Cloud resource management using adaptive firefly algorithm and artificial neural network

Cloud resource management using adaptive firefly algorithm and artificial neural network

An Improved Fuzzy Adaptive Firefly Algorithm-Based Hybrid Clustering Algorithms

The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a novel fuzzy adaptive fuzzy firefly algorithm that significantly improves the accuracy and convergence rate while comparing with the existing optimization algorithms. Further, the proposed algorithm fused with existing hybrid clustering algorithms involving fuzzy set, intuitionistic fuzzy set, and rough set resulted in eight novel hybrid clustering algorithms which lead to better performance in optimizing the selection of initial centroids. To validate the proposal, experimental studies have been conducted on datasets found in bench-marking data repositories such as UCI, and Kaggle. The performance and accuracy evaluation of proposed algorithms have been carried out with the aid of seven accuracy measures. Results clearly indicate the improved accuracy and convergence rate of the proposed algorithms.

A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy

Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur's entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.

Optimal resource allocation with deep reinforcement learning and greedy adaptive firefly algorithm in cloud computing

AbstractThe allocation of resources is a foremost demanding task in cloud computing. Scholars are yet finding it difficult to allocate appropriate resources to the set of user tasks. Our objective is to provide a platform that optimizes a dynamic resource allocation scheme. Multi‐agent deep reinforcement learning‐based greedy adaptive firefly algorithm (MAD‐GAF) has been proposed herein includes both the resource management and allocation techniques. This chooses the best Quality of Service (QoS) measured host for a group of tasks efficiently and subsequently minimize the task execution time. The proposed cloud brokering architecture comprises a multi‐agent system, the cloud provider and the user. Initially, deep reinforcement learning has been built to recreate the request of cloud customers by forecasting the value of unused resources. Then the recreated customer request is forwarded to the global broker agent, which maps the virtual machine (VM) to the most appropriate cluster of physical machine (PM). The virtual machine monitor (VMM) selects VMs by managing and accessing the physical resources. The global utility agent allocates VMs using the GAF optimization algorithm, which specifies the best QoS measured host to decrease the whole tasks' average response time, thus optimizing resource allocation compared to the current approaches.

A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy

Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur's entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.

A new path planning method of mobile robot based on adaptive dynamic firefly algorithm

Path planning has always been a hot topic in the field of mobile robot research. At present, the mainstream issues of the mobile robot path planning are combined with the swarm intelligence algorithms. Among them, the firefly algorithm is more typical. The firefly algorithm has the advantages of simple concepts and easy implementation, but it also has the disadvantages of being easily trapped into a local optimal solution, with slow convergence speed and low accuracy. To better combine the path planning of mobile robot with firefly algorithm, this paper studies the optimization firefly algorithm for the path planning of mobile robot. By using the strategies of optimizing the adaptive parameters in the firefly algorithm, an adaptive firefly algorithm is designed to solve the problem that the firefly algorithm is easy to get into the local optimal solution and improves the performance of firefly algorithm. The optimized algorithm with high performance can improve the computing ability and reaction speed of the mobile robot in the path planning. Finally, the theoretical and experimental results have verified the effectiveness and superiority of the proposed algorithm, which can meet the requirements of the mobile robot path planning.

A staged adaptive firefly algorithm for UAV charging planning in wireless sensor networks

A staged adaptive firefly algorithm (SAFA) is proposed in this paper. Firstly, the attraction model is improved to promote the convergence of the algorithm in the case of small algorithm complexity. Secondly, three adaptive adjustment functions of parameters are established according to the actual conditions of convergence and iteration. Because of the new attraction model, SAFA has better population diversity at the early stage of iteration and can carry out adaptive balance and adjustment of global and local optimization at the late stage of iteration. Because of three adaptive adjustment functions of parameters, SAFA has better randomness and non-repeatability of parameters, so it has stronger global convergence ability. To verify the performance, SAFA algorithm is compared with other four algorithms in testing six standard functions and unmanned aerial vehicle (UAV) charging path planning for wireless sensor network in this paper. A large number of experimental results show that the precision and convergence speed of SAFA is higher than that of the other four algorithms.

English

English

A dynamic adaptive firefly algorithm with globally orientation

This paper proposes a dynamic adaptive firefly algorithm to overcome the disadvantages of the standard firefly algorithm, to improve the convergence rate and solution precision, and to avoid the premature algorithm trapping at the local extreme. It has a global-oriented moving mechanism and can dynamically adjust the step size and attractiveness. First, through the adaptive deviation degree strategy of optimal distance combining with the Gaussian distribution, it optimizes the fixed step-factor to balance the exploration and excavation capabilities of the algorithm and improves the diversity of the population. Second, minimum attractiveness is introduced to the algorithm, and is adaptively changed with the number of iterations, which can avoid random walk due to lack of traction between fireflies. Finally, this paper improves the mobility mechanism based on the position of the current optimal firefly. It enables firefly move with global orientation and also expands the sharing of information between fireflies to improve the overall evolutionary optimization performance of the algorithm. Theoretical analysis proves the convergence and time complexity of the improved algorithm. The simulation results of several test functions and engineering constraint optimization problems show that the improved algorithm has better solution performance, and clearly improves the convergence speed and solution accuracy.

Parameters Tuning of Adaptive Firefly Algorithm based Strategy for t-way Testing

The adaptive firefly algorithm (AFA) is developed based on an elitism operator. Elitism operators can perform the function of updating the effectiveness of diversification in a search algorithm. In this study, a strategy was proposed to upgrade the FA concerning static issues. Most traditionally, for evolutionary algorithms, elitism suggests that the best solution found is utilized to work for the next generation. Elitism involves the replication of a small set of the fittest candidate solutions, which remain unaltered, into succeeding generations. The condition can radically impact execution time by ensuring that the El waste no time on re-finding newly-disposed partial solutions. Candidates who stay protected and unmodified via elitism all meet the requirements for parent selection in terms of rearing the remainder of the succeeding generation. This study used different tuning parameters, such as the number of fireflies, iterations and switching probability. To ensure that AFA could perform for t-way testing as useful as other strategies to generate the best performance. Considering the standard covering array (N, 2, ) it demonstrates the tuning parameters for AFA to improve elitism. In this paper, the Findings show that AFA, as well as t-way testing, can deliver the minimum requirements and sufficient results

Adaptive parameter tuning for agent-based modeling and simulation

The purpose of this study was to solve the parameter-tuning problem of complex systems modeled in an agent-based modeling and simulation environment. As a good set of parameters is necessary to demonstrate the target behavior in a realistic way, modeling a complex system constitutes an optimization problem that must be solved for systems with large parameter spaces. This study presents a three-step hybrid parameter-tuning approach for agent-based models and simulations. In the first step, the problem is defined; in the second step, a parameter-tuning process is performed using the following meta-heuristic algorithms: the Genetic Algorithm, the Firefly Algorithm, the Particle Swarm Optimization algorithm, and the Artificial Bee Colony algorithm. The critical parameters of the meta-heuristic algorithms used in the second step are tuned using the adaptive parameter-tuning method. Thus, new meta-heuristic algorithms are developed, namely, the Adaptive Genetic Algorithm, the Adaptive Firefly Algorithm, the Adaptive Particle Swarm Optimization algorithm, and the Adaptive Artificial Bee Colony algorithm. In the third step, the control phase, the algorithm parameters obtained via the adaptive parameter-tuning method and the parameter values of the model obtained from the meta-heuristic algorithms are manually provided to the developed tool performing the parameter-tuning process and they are tested. The best results are achieved when the meta-heuristic algorithms that were successful in the optimization process are used with their critical parameters adjusted for optimum results. The proposed approach is tested by using the Predator–Prey model, the Eight Queens model, and the Flow Zombies model, and the results are compared.

Energy analysis and multi-objective optimization of waste heat and cold energy recovery process in LNG-fueled vessels based on a triple organic Rankine cycle

Due to the high level of pollutant emissions from traditional marine diesel engines, Liquefied Natural Gas (LNG) as clean energy is becoming a better choice for main engines to replace the traditional fuels. Meanwhile, in order to improve the energy efficiency of the marine power system, the Organic Rankine Cycle (ORC) has been regarded as the most suitable solution to recover the waste heat for the power generation of vessels. In this paper, both the waste heat of the main engine and the cold energy of LNG have been fully considered, and a novel triple ORC process has been proposed for the waste heat and cold energy recovery of LNG-fueled vessels. It adopts the exhaust gas of the main engine and the cooling water from the engine jacket as heat sources, and uses the cold energy of LNG and the sea water as cold sources. Based on the 15 optional working fluid conditions, the heat source utilization rate, system exergy efficiency, net output power, and system cost are, respectively, combined as two objectives, and the multi-objective adaptive firefly algorithm is used to optimize the thermodynamic performance of the system. The optimization results of different heat and cold sources as well as the design parameters have been discussed. Finally, the system's exergy loss has been analyzed to make suggestions for further improvement. The results show that this novel ORC system can better meet the energy recovery requirements of LNG-fueled vessels, with higher net output power, lower cost, and greater energy recovery efficiency. The largest exergy loss of the system exists in the condensers of the stages 2 and 3, and the expanders in the various stages. Therefore, subsequent cooling energy recovery and the use of Stirling engines can be considered to further improve the system efficiency.