Migration Optimization Algorithm Research Articles

Shrimp classification for white spot syndrome detection through enhanced gated recurrent unit-based wild geese migration optimization algorithm.

The major dangerous viral infection for cultivated shrimps is WSSV. The virus is extremely dangerous, spreads swiftly, and may result in up to 100% mortality in 3-10days. The vast wrapped double stranded DNA virus known as WSSV describes a member of the Nimaviridae viral family's species Whispovirus. It impacts a variety of crustacean hosts but predominantly marine shrimp species that are raised for commercial purposes. The entire age groups are affected by the virus, which leads to widespread mortality. Mesodermal and ectodermal tissues, like the lymph nodes, gills, and cuticular epithelium, represents the centres of infection. Complete genome sequencing related to the WSSV strains from Thailand, China, and Taiwan has identified minute genetic variations amongst them. There exist conflicting findings on the causes of WSSV pathogenicity, which involve variations in the size associated with the genome, the count of tandem repeats, and the availability or lack of certain proteins. Hence, this paper plans to perform the shrimp classification for the WSSV on the basis of novel deep learning methodology. Initially, the data is gathered from the farms as well as internet sources. Next, the pre-processing of the gathered shrimp images is accomplished using the LBP technique. These pre-processed images undergo the segmentation process utilizing the TGVFCMS approach. The extraction of the features from these segmented images is performed by the PLDA technique. In the final step, the classification of the shrimp into healthy shrimp and WSSV affected shrimp is done by the EGRU, in which the parameter tuning is accomplished by the wild GMO algorithm with the consideration of accuracy maximization as the major objective function. Performance indicators for accuracy have been compared with those of various conventional methods, and the results show that the methodology is capable of accurately identifying the shrimp WSSV illness.

Robust Integrated Data and Energy Transfer Aided by Intelligent Reflecting Surfaces: Successive Target Migration Optimization Toward Energy Sustainability

Intelligent reflecting surfaces (IRSs) can actively adjust the wireless environment. However, accurate channel estimation on IRS-aided communication systems is difficult to obtain. Therefore, we study a robust beamforming design for an IRS-aided integrated data and energy transfer (IDET) with imperfect channel state information (CSI). Against the uncertain channel estimation error, we robustly design the transmit beamformers of the transmitter and the passive reflecting beamformer of the IRS to minimize the transmit power by satisfying both the wireless data transfer (WDT) and wireless energy transfer (WET) requirements for realising energy-sustainability in 6G. A successive target migration optimization (STMO) algorithm is proposed to obtain a robust design. The transmit covariance matrices are optimized by relaxing rank-one constraints, when a passive reflecting beamformer is given. Then, the target to minimize the transmit power is migrated to maximize the QoS requirements of energy users due to the fixed transmit power. A local optimal reflecting beamformer is obtained for improving the attainable WET performance, when the transmit covariance matrices are given. Finally, we prove that the rank-one transmit beamformers can always be found, which have the same WET and WDT performance as the transmit covariance matrices. The numerical results demonstrate the advantage of our design.

Multi-Machine Power System Transient Stability Enhancement Utilizing a Fractional Order-Based Nonlinear Stabilizer

Given the intricate nature of contemporary energy systems, addressing the control and stability analysis of these systems necessitates the consideration of highly large-scale models. Transient stability analysis stands as a crucial challenge in enhancing energy system efficiency. Power System Stabilizers (PSSs), integrated within excitation control for synchronous generators, offer a cost-effective means to bolster power systems’ stability and reliability. In this study, we propose an enhanced nonlinear control strategy based on synergetic control theory for PSSs. This strategy aims to mitigate electromechanical oscillations and rectify the limitations associated with linear approximations within large-scale energy systems that incorporate thyristor-controlled series capacitors (TCSCs). To dynamically adjust the coefficients of the nonlinear controller, we employ the Fractional Order Fish Migration Optimization (FOFMO) algorithm, rooted in fractional calculus (FC) theory. The FOFMO algorithm adapts by updating position and velocity within fractional-order structures. To assess the effectiveness of the improved controller, comprehensive numerical simulations are conducted. Initially, we examine its performance in a single machine connected to the infinite bus (SMIB) power system under various fault conditions. Subsequently, we extend the application of the proposed nonlinear stabilizer to a two-area, four-machine power system. Our numerical results reveal highly promising advancements in both control accuracy and the dynamic characteristics of controlled power systems.

Energy consumption prediction by modified fish migration optimization algorithm: City single-family homes

The main objective of this paper is to design energy consumption modeling systems to predict the energy consumption value due to today's energy use in cities increasing. A new Modified Fish Migration Optimization Algorithm-based Numerical Moment Matching (MFMOA-NMM) technique is applied as an initial indeterminacy evaluation method to forecast the electrical power use in large-scale datasets of single-family residential homes with the application of main characteristics. To develop energy modeling, the DesignBuilder is used. The energy performance of the homes is assessed by combining these techniques. With the application of the information from the energy audit and data of the assessor (8368 single-family homes), four effective parameters including attic insulation R-value, the efficiency of air conditioners (SEER), type of the window, and the area of homes are inputted into the model improvement. Waterloo in Iowa is selected as the case study. The results showed that the yearly electrical power use expected is equal to 10,195 kWh which is within 5% of the experimental electrical power use. Also, the monthly expected electrical power use Average Bias Error is equal to 2.4% and the Coefficient of Variation with the Root Average Square Error is equal to 6.7%. This technique can be applied to create a small group of typical buildings to show the energy performance of a bigger group of buildings.

An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

Early detection of melanoma is crucial in preventing death from this fatal skin cancer. Therefore, it would be valuable to develop a method that facilitates this process. The diagnosis of melanoma typically involves an invasive form of testing called a biopsy, as well as non-invasive intelligent approaches to diagnosis. In the present study a recent research, a novel approach has been developed for the optimal detection of melanoma cancer. The method uses reinforcement learning for segmenting the skin regions, followed by the extraction and selection of useful features using the Enhanced Fish Migration Optimizer (EFMO) algorithm. The outcomes get categorized on the basis of an optimized SVM on the basis of the EFMO algorithm. The recommended approach has been certified by applying it to the SIIM-ISIC dataset of Melanoma and comparing it with 12 other approaches. Simulations illustrated that the proposed method delivered the finest values compared to the others.

Cluster head selection strategy of WSN based on binary multi-objective adaptive fish migration optimization algorithm

In deployed wireless sensor networks (WSNs), how to efficiently transmit information collected by sensor nodes with limited energy is a challenging problem. An appropriate cluster head selection strategy can efficiently solve this problem, but there are many factors to be considered, such as energy consumption, the coverage of cluster head nodes, and the number of cluster head nodes. Each factor has a profound impact on the performance of wireless sensor networks, and there are conflicts among them. In order to solve the conflict of multiple factors and obtain the optimal selection strategy of the cluster head node, this paper proposes a Binary Multi-Objective Adaptive Fish Migration Optimization (BMAFMO) algorithm. The algorithm introduces the Pareto optimal solution storage strategy to improve the global search ability of the optimization algorithm and transform the continuous solution into a binary solution according to the sigmoid transformation function to solve the problem of cluster head node selection. The new algorithm was comprehensively tested using eight test problems and four test metrics. At the same time, the reliability of the algorithm is tested by rank sum test. The test results show that the BMAFMO algorithm obtained the best results in 78.13% test problems compared with other algorithms. Finally, the BAMFMO algorithm is applied to solve the cluster head selection problem of WSN and the simulation results show the novel algorithm has better optimization ability than other heuristic algorithms.

A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security

Ensuring the privacy and trustworthiness of smart city—Internet of Things (IoT) networks have recently remained the central problem. Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. However, it limits the critical problems of high time complexity, computational cost, difficulty to understand, and reduced level of security. Therefore, the proposed work intends to implement a group of novel methodologies for developing an effective Cyborg intelligence security model to secure smart city systems. Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. The original contribution of this work is to develop a novel Cyborg intelligence framework for protecting smart city networks from modern cyber-threats. In this system, a combination of unique and intelligent mechanisms are implemented to ensure the security of smart city networks. It includes QIDI for data filtering, CSOM for feature optimization and dimensionality reduction, and RMML for categorizing the type of intrusion. By using these methodologies, the overall attack detection performance and efficiency have been greatly increased in the proposed cyborg model. Here, the main reason of using CSOM methodology is to increase the learning speed and prediction performance of the classifier while detecting intrusions from the smart city networks. Moreover, the CSOM provides the optimized set of features for improving the training and testing operations of classifier with high accuracy and efficiency. Among other methodologies, the CSOM has the unique characteristics of increased searching efficiency, high convergence, and fast processing speed. During the evaluation, the different types of cyber-threat datasets are considered for testing and validation, and the results are compared with the recent state-of-the-art model approaches.

Wireless Sensor Deployment Based on Multiobjective Adaptive Fish Migration Optimization

Wireless Sensor Network (WSN) is a powerful tool to help humans monitor a specific area, and the deployment strategy of sensors profoundly determines the performance of WSN. How to find the best deployment method has become the research topic for many scholars. The deployment strategy aims to expand the deployment scope, reduce energy consumption, and reduce duplicate coverage areas. Many multiobjective heuristic algorithms have been proposed to solve this problem. This paper proposes a multiobjective adaptive fish migration optimization (MAFMO) algorithm, which adds an adaptive-based repository and crowding degree-selection strategy for multiobjective optimization. The simulation results reveal that the MAFMO algorithm has more advantages in malleability and distribution than other famous algorithms. Finally, the algorithm is applied to the WSN deployment problem, and the simulation results are compared with other algorithms. The results show that a better solution can be found using MAFMO.

A Novel Model Prediction and Migration Method for Multi-Mode Nonlinear Time-Delay Processes

Most industrial processes have nonlinear and time-delay characteristics leading to difficulty in prediction modeling. In addition, the working conditions of most industrial processes are complex, which results in multiple modes. Testing and modeling for each mode is a waste of time and resources. Therefore, it is urgent to complete model migration between different modes. In this work, a new prediction model, a nonlinear autoregressive model with exogenous inputs and back propagation neural network (NARX-BP), is proposed for the nonlinear and time-delay processes, where the input data order of the model is determined by the feedforward neural network (FNN) method, and the nonlinear relation is realized by the BP neural network. For the multi-mode characteristic, a new migration optimization algorithm, input–output slope/bias correction and differential evolution (IOSBC-DE), is provided for using a small amount of data under a new mode to correct the slope and bias of the relationship between the input and output variables through DE. The modeling and migration methods are applied to a wind tunnel system, and the simulation result shows the effectiveness of the proposed method.

Corrigendum to “Development of a boundary assigned animal migration optimization algorithm and its application to optimal power flow study” [Expert Syst. Appl. 200 (2022) 116776

Corrigendum to “Development of a boundary assigned animal migration optimization algorithm and its application to optimal power flow study” [Expert Syst. Appl. 200 (2022) 116776

Harmonics Elimination in 7-Level Multilevel Inverter Using Animal Migration Optimization Algorithm with Different Objective Functions

Harmonics can degrade the power quality of a multilevel inverter by causing the voltage to be distorted and vary from sinusoidal waveforms. Harmonics can be reduced by increasing the number of voltage levels or by employing suitable modulation techniques. In this paper, The Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) modulation method is employed to obtain the optimal switching angles that able to reduce the specific individual harmonic and the Total Harmonic Distortion (THD) in singlephase 7-level Cascaded H-Bridge multilevel inverter. The Animal Migration Optimization (AMO) is proposed to acquire these angles using two difference objective functions. The performance is examined and evaluated. Both objective functions able to determine the optimal switching angles starting from modulation index of 0.34. However, the comparative study demonstratethat objective function number 2 has better performance in term of lowering selective individual harmonics as well as THD.

Elitist Animal Migration Optimization for Protein Structure Prediction based on 3D Off-Lattice Model

Predicting the structure of protein has been the center of attraction for the researchers. The aim is to make a reliable prediction of the protein structure by obtaining the minimum energy values among amino acids interactions. According to the generated shape of amino acids, the functionality of the proteins can be determined. However, it is known as one of the most challenging tasks in the field of bioinformatics considering its high computation complexity. Metaheuristic algorithms are mainly preferred by researchers from various fields, since their performances are quite satisfactory in solving such complex problems. Animal Migration Optimization (AMO) algorithm is a metaheuristic approach which mimics the behavior of animals during the migration process. However, in this research to reach a high solution quality, an elitist version of Animal Migration Optimization (ELAMO) algorithm is considered and in particular it is applied to Protein Structure Prediction (PSP) problem. The performance of ELAMO is tested on some well-studied artificial and real protein sequences, and then compared with powerful optimization algorithms which are specially designed for solving PSP problem. The results show that ELAMO is quite capable in solving this problem. Hence, it can be used as an efficient optimizer for solving complex problems that require better solution quality in the field of bioinformatics.

Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot.

This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots.

Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks.

Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.

Development of a Boundary Assigned Animal Migration Optimization algorithm and its application to optimal power flow study

A new variant of Animal Migration Optimization (AMO) algorithm known as Boundary Assigned Animal Migration Optimization (BA-AMO) has been conceptualized to study the optimal power flow problem relating to IEEE bus systems with reference to fuel cost minimization of generators, total voltage deviation minimization at buses and power loss minimization in lines. The proposed method is validated against few benchmark functions and then applied to some case studies in the power system domain. The performance of the proposed technique is compared with quite a number of well established nature inspired meta-heuristic algorithms and significant improvement in results have been obtained. This clearly justifies the capability of the proposed strategy to be an efficient competitor in optimization domain.

Energy-conscious flexible job shop scheduling problem considering transportation time and deterioration effect simultaneously

In recent years, research on green manufacturing has received much attention to the increasing environmental problems. Production scheduling is viewed as an effective way of saving energy in manufacturing system from the operation management point of view. This paper investigates an energy-conscious flexible job shop scheduling problem with transportation time and deterioration effect simultaneously (ECFJSP-TD). To begin with, a mathematical model is built with the objective of optimizing total energy consumption. A modified animal migration optimization algorithm (MAMO) is elaborately designed by considering the characteristics of the problem. In the MAMO, a two-vector encoding is designed to represent the scheduling solution, and a left-shift decoding approach is proposed to ensure the operation permutation on each machine as compact as possible. In addition, a new animal migration operator is designed based on the problem-specific neighborhood structures, which can dynamically adjust the search scope along with the iteration process to balance global exploration and local exploitation. A new population updating operator is developed based on the crossover operation to generate new individuals by absorbing some information about the best individuals as well as two random individuals. Finally, comprehensive experiments are carried out to test the performance of the proposed MAMO algorithm. According to the comparison data, the MAMO is more effective for solving the considered problem than some published algorithms.

Parallel fish migration optimization with compact technology based on memory principle for wireless sensor networks

This paper proposes a parallel fish migration optimization algorithm with compact technology (PCFMO), and designs a sequential communication strategy between groups and a compact technology to save memory space. This paper uses 30 benchmark functions on CEC 2014 and three engineering problems as test benchmarks to compare the PCFMO algorithm with seven well-known algorithms, including Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Harris Hawks Optimization (HHO), Salp Swarm Algorithm (SSA), Fish Migration Optimization (FMO), Archimedes Optimization Algorithm (AOA) and Aquila Optimizer (AO). The experimental results show that the PCFMO algorithm achieves better results and has less space occupied by the population. The comprehensive performance of wireless sensor networks (WSN) is challenged by the battery energy limitation of sensor nodes distributed in specific areas. A proper cluster head set can manage energy consumption reasonably to extend the life cycle of the sensor network and increase the amount of message transmission. This paper takes the energy consumption in each round as the fitness function and adds the memory principle to the PCFMO algorithm to speed up the search for the optimal cluster head set. Compared with the LEACH, HFAPSO and PSO-C algorithms, the PCFMO algorithm based on the memory principle (PCFMO-Memory) can speed up the convergence of finding the optimal cluster head set, extend the life cycle of WSN and increase the amount of message transmission.

Organizational Resource Allocation by Mobile Edge Computing in the Context of the Internet of Things

In recent years, the number of electronic devices and users has increased sharply with the rapid development and progress of electronic information technology. The motivation of this paper is to optimize the organization’s resource allocation strategy in the Internet of Things (IoT) environment. The optimal path planning and information processing efficiency are improved through Unmanned Aerial Vehicle (UAV) technology and migration optimization algorithm. The research method is migration optimization algorithm and UAV dynamic network. The information processing capability of traditional wireless communication systems has gradually been unable to meet the actual information processing needs. Therefore, a static network migration algorithm is constructed based on multiple-user multilateral edge computing servers. It migrates each user’s information processing task to the neighboring edge confidence processing server and uses the information processing server to perform auxiliary calculations. The simulation model adds a utility function that simulates energy consumption, delay weighting, and maximum extreme value, combined with the allocation strategy of optimizing each user’s information processing task to achieve the optimization goal. The static network migration algorithm established in this simulation has better results than other benchmark algorithms. Both scenario 1 and scenario 2 in the simulation show very close performance to the optimal solution. Meanwhile, a migration algorithm that can provide wireless charging for UAVs is built by a dynamic edge computing model based on the time associated with the UAV base station and multiple end users. Combined with completing the information processing tasks in each time slot, the energy arrival is also non-directional. The dynamic network migration algorithm can optimize the number of tasks absorbed by the end-user based on the current online status of the system without knowing the global information. The optimized target equation is related to the queue stability, and the parameter V has a linear relationship with the queue backlog length. Here, the problem of computing migration is studied in Mobile Edge Computing (MEC). The results show that the utility function of the weighted sum has an approximately linear relationship with the weights. As the value of the utility function increases, so does the weight function. The optimal data throughput of the proposed model is 70,000 bits, while the optimal data throughput of the state-of-the-art model is 68,000 bits. Therefore, the data transmission performance of the model presented here is better than that of other models. MEC can be significantly improved the efficiency of organizational resource allocation. Combining UAV and wireless charging technology, the computing and communication resource allocation issues of the UAV’s edge computing system are comprehensively discussed to improve the performance and efficiency of the network.

Application of Binary Adaptive Migration Optimization Algorithm to Identify Parkinson's Disease

Application of Binary Adaptive Migration Optimization Algorithm to Identify Parkinson's Disease

An Improved Animal Migration Optimization Algorithm to Train the Feed-Forward Artificial Neural Networks.

The most important and demanding part of the artificial neural network is the training process which involves finding the most suitable values for the weights in the network architecture, a challenging optimization problem. Gradient approaches and the meta-heuristic approaches are two methods extensively used to optimize the weights in the network. Gradient approaches have serious disadvantages including getting stuck in local optima, inadequate exploration, etc. To overcome these disadvantages, meta-heuristic approaches are preferred in training the artificial neural network instead of gradient methods. Therefore, in this study, an improved animal migration optimization algorithm with the Lévy flight feature was proposed to train the multilayer perceptron. The proposed hybrid algorithm is named IAMO-MLP. The main contributions of this article are that the IAMO algorithm was developed, the IAMO-MLP algorithm can successfully escape from local optima, and the initial positions did not affect the performance of the IAMO-MLP algorithm. The enhanced algorithm was tested and validated against a wider set of benchmark functions and indicated that it substantially outperformed the original implementation. Afterward, the IAMO-MLP was compared with ten algorithms on five classification problems (xor, balloon, iris, breast cancer, and heart) and one real-world problem in terms of mean squared error, classification accuracy, and nonparametric statistical Friedman test. According to the results, the IAMO was successful in training the multilayer perceptron.