Animal Migration Optimization Research Articles

An Efficient Authentication Approach with Optimization Algorithms and Elliptical Curve Cryptography for Cloud Environment

The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. Security is very important for any kind of networks. As a main communication mode, the security mechanism for multicast is not only the measure to ensure secured communications, but also the precondition for other security services. Attacks are one of the biggest concerns for security professionals. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies. This paper presents a dual optimizer based key generation method for the improving the authentication with Elliptical Curve Cryptography (ECC) encryption algorithm. The optimal private and secret key for the encryption and decryption are obtained with the optimization techniques like Animal Migration Optimization (AMO), and Brain Storm Optimization (BSO) for strengthening the security in the Cloud Computing environment.

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.

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.

A discrete animal migration algorithm for dual-resource constrained energy-saving flexible job shop scheduling problem

This paper presents a discrete animal migration optimization (DAMO) to solve the dual-resource constrained energy-saving flexible job shop scheduling problem (DRCESFJSP), with the aim of minimizing the total energy consumption in the workshop. A job-resource-based two-vector encoding method is designed to represent the scheduling solution, and an energy-saving decoding approach is given based on the left-shift rule. To ensure the quality and diversity of initial scheduling solutions, a heuristic approach is employed for the resource assignment, and some dispatching rules are applied to acquire the operation permutation. In the proposed DAMO, based on the characteristics of the DRCESFJSP problem, the search operators of the basic AMO are discretized to adapt to the problem under study. An animal migration operator is presented based on six problem-based neighborhood structures, which dynamically changes the search scale of each animal according to its solution quality. An individual updating operator based on crossover operation is designed to obtain new individuals through the crossover operation between the current individual and the best individual or a random individual. To evaluate the performance of the proposed algorithm, the Taguchi design of experiment method is first applied to obtain the best combination of parameters. Numerical experiments are carried out based on 32 instances in the existing literature. Computational data and statistical comparisons indicate that both the left-shift decoding rule and population initialization strategy are effective in enhancing the quality of the scheduling solutions. It also demonstrate that the proposed DAMO has advantages against other compared algorithms in terms of the solving accuracy for solving the DRCESFJSP.

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.

Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study, we introduce a new hybrid wavelet filter for accurate image enhancement. The main objective of enhancement is to produce quality images for detecting cancer sections in images. Image enhancement is the main step where the quality of the input image is improved to detect cancer masses. In this study, we use a combination of two filters, namely, Gabor and Legendre. The edges are detected using the Canny detector to smoothen the images. High-quality enhanced image is obtained through the Gabor–Legendre filter (GLFIL) process. Further image is used by classification algorithm. Animal migration optimization with neural network is implemented for classifying the image. The output is compared to existing filter techniques. Ultimately, the accuracy achieved by the proposed technique is 98%, which is higher than existing algorithms.

An Early Breast Cancer Detection System Using Recurrent Neural Network (RNN) with Animal Migration Optimization (AMO) Based Classification Method

Breast cancer can be detected using early signs of it mammograms and digital mammography. For Computer Aided Detection (CAD), algorithms can be developed using this opportunities. Early detection is assisted by self-test and periodical check-ups and it can enhance the survival chance significantly. Due the need of breast cancer’s early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. So, it requires a non-invasive cancer detection system, which is highly effective, accurate, fast as well as robust. Proposed work has three steps, (i) Pre-processing, (ii) Segmentation, and (iii) Classification. Firstly, preprocessing stage removing noise from images by using mean and median filtering algorithms are used, while keeping its features intact for better understanding and recognition, then edge detection by using canny edge detector. It uses Gaussian filter for smoothening image. Gaussian smoothening is used for enhancing image analysis process quality, result in blurring of fine-scaled image edges. In the next stage, image representation is changed into something, which makes analyses process as a simple one. Foreground and background subtraction is used for accurate breast image detection in segmentation. After completion of segmentation stage, the remove unwanted image in input image dataset. Finally, a novel RNN forclassifying and detecting breast cancer using Auto Encoder (AE) based RNN for feature extraction by integrating Animal Migration Optimization (AMO) for tuning the parameters of RNN model, then softmax classifier use RNN algorithm. Experimental results are conducted using Mini-Mammographic (MIAS) dataset of breast cancer. The classifiers are measured through measures like precision, recall, f-measure and accuracy.

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.

Structural system reliability analysis based on multi-modal optimization and saddlepoint approximation

This paper studies reliability analysis of structures with multiple failure modes. The analysis consists of three parts. First, the memetic animal migration optimization algorithm, applied for multiple optimum points detection in a multimodal optimization problem, is used to search for the most probable points of the limit state functions of main failure modes. Next, the multi-point first-order reliability method is used to estimate the failure probability of structures with multiple failure modes. Last, the saddlepoint approximation based second-order reliability method is further extended to system reliability analysis, aiming to improve the accuracy of system reliability prediction.

Cockroach Swarm Optimization Algorithm for High Utility Association Rule Mining

Association rule mining is an important and widely used data mining technique. It is used to retrieve highly related objects in a database based on the occurrence. Recently, utility-based association rules were proposed to consider significant factors of the object. The main objective of this research work is to retrieve high utility association rules from a database using cockroach swarm optimization algorithm. So far, in the literature, no optimization algorithm was proposed in utility-based association rule mining. In this research work, CSOUAR (cockroach swarm optimization for high utility association rule mining) algorithm was proposed to generate utility association rules. CSOUAR algorithm is based on three behaviours of cockroach: chase-swarming, dispersing, and ruthless. To analyse the performance of CSOUAR, an improved particle swarm optimization (PSO-UAR), animal migration optimization (AMO-UAR), bees swarm optimisation (BSO-UAR), and penguins search optimisation (peSO-UAR) are also proposed in this work.

Mobility Aware Quality Enhanced Cluster Based Routing Protocol for Mobile Ad-Hoc Networks Using Hybrid Optimization Algorithm

Wireless Sensor Networks (WSN) utilizes more nodes to forward messages and information from source node to destination node and these nodes are battery-powered devices. Thus, Energy saving is always vital role of Routing Protocols which need to maximize the lifetime of WSN. Mobile ad hoc networks (MANET) are self-starting and self-constructing multichip wireless networks in which, the assembly of the system deviates vigorously. This is mainly due to the mobility of the nodes. The nodes in the network not only act as hosts but also as routers that route data from other nodes in network. MANETs require an efficient routing protocol that achieves the quality of service (QoS) mechanism. They are not considering the mobility as one constraint for those problems. Because, mobility of nodes causes frequent link failures and high error rates, so it makes difficult to maintain the desired QoS in the network. To overcome those problems, we propose mobility aware routing protocol for MANET using hybrid optimization (MARP-HO) algorithm, which maximizes the QoS in data transmission. The proposed routing protocol comprises of two phases. The clustering process consists of cluster formation and cluster head (CH) selection. First, the energy efficient clustering is done by improved animal migration optimization (IAMO) algorithm. Then, we gather multiple constraints are energy consumption, received signal strength, mobility and cooperative rate from each cluster member. The gathered constraints used to compute the node cost of each cluster member, here the highest cost node act as CH of cluster. Second, we compute routing path between source–destination using improved ant colony optimization (IACO) algorithm. The proposed MARP-HO routing protocol is compared with existing state-of-art routing protocols in terms of energy consumption, delivery ratio, loss ratio, link stability, delay, network lifetime, number of dead nodes and throughput.

A memetic animal migration optimizer for multimodal optimization

Unimodal optimization algorithms can find only one global optimum solution, while multimodal ones have the ability to detect all/most existing local/global optima in the problem space. Many practical scientific and engineering optimization problems have multiple optima to be located. There are a considerable number of optimization approaches in the literature to address the unimodal problems. Although multimodal optimization methods have not been studied as much as the unimodal ones, they have attracted an enormous amount of attention recently. However, most of them suffer from a common niching parameter problem. The main difficulty faced by existing approaches is determining the proper niching radius. Determining the appropriate radius of the niche requires prior knowledge of the problem space. This paper proposes a novel multimodal optimization scheme that does not face the dilemma of having prior knowledge of the problem space as it does not require the niching parameter to be determined in advance. This scheme is the extended version of the unimodal animal migration optimization (AMO) algorithm that has the capability of taking advantage of finding multiple solutions. Like other multimodal optimization approaches, the proposed MAMO requires specific modifications to make it possible to locate multiple optima. The local neighborhood policy is modified to adapt the multimodal search by utilizing Coulomb's law. Also, Coulomb's law is also applied to decide the movement direction of the individuals. Hence, instead of moving an individual toward the two randomly chosen individuals, it moves toward the near and good enough two neighborhoods. Additionally, a further local search step is performed to improve the exploitation. To investigate the performance of the MAMO, the comparisons are conducted with five existing multi-modal optimization algorithms on nine benchmarks of the CEC 2013 competition. The experimental results reveal that the MAMO performs success in locating all or most of the local/global optima and outperforms other compared methods. Note that the source codes of the proposed MAMO algorithm are publicly available at www.taymaz.dev .

WITHDRAWN: Integrated feature selection (IFS) algorithm and enhanced weight based convolutional neural network (EWCNN) for social emotion classification

Abstract To design and implement IFS-EWCNN for improve the performance of social emotion classification for twitter reviews. Design a new Integrated Feature Selection (IFS) algorithm, which combines filter and wrapper methods procedure for evaluating semantic gap among source features produced using teaching model and target task characteristics before the application of sparse encoding to transfer learning. Filtering algorithm follows the procedure of the information gain and Pearson’s Correlation. In the wrapper selection algorithm follows the procedure of the Expectation Maximization with Forward Search (EMFS), and Exhaustive feature selection. Then implemented a novel Animal Migration Optimization based Sparse Encoding (AMO-SC) for forming dense high-level features by transforming sparse low-level features and further investigation is required on effectiveness of this method on emotion classification. Finally, for classifications of emotions, proposed an Enhanced Weight based Convolutional Neural Network (EWCNN). It concludes that proposed IFS-EWCNN classifier achieves better performance compared with the existing methods in terms of f-measure, specificity, sensitivity, recall, precision and accuracy. The experiments are carried out in SemEval 2016 Task 4A for sentiment and SemEval 2018 Task 1C emotion classification using Matlab tool. The proposed IFS-EWCNN classifier approaches accurately classify the social emotions.

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders.

The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.

An Efficient Association Rule Mining Algorithm Based on Animal Migration Optimization Processing of Unknown Incidents in Crime Analysis Brance

A new protection scheme was proposed to avoid this problem. It is intended that each node uses a salted and a not salted HLL. If their estimates differ considerably, an attacker attempts to manipulate the estimates of HLL. In addition to avoiding manipulation, the proposed salted and unsalted (SNS) regime can also detect attempts at manipulation. A practical configuration showing how manipulation attempts can be detected in a low false positive probability has been shown to be applicable to this SNS scheme. Therefore, if merge ability is to be preserved it can be an interesting approach to protect HLLs from avoidance. In this paper the proposal for a new mining algorithm based on Animal Migration Optimization is made to decrease the number of Association Rules called ARM-AMO. The idea is to remove from the data rules which are not highly supportive and unnecessary. First of all, common item sets and association rules are generated with an Apriori algorithm. AMO also reduces the number of association rules incorporated in a new fitness function. In here, we provide a well-organized mechanism for incident derivation under the unwanted incident. This mechanism very useful for measure the heavy load of an incoming incident and exact calculation of the probability. In additional method is a Select-ability mechanism, which performs an important responsibility in incident derivation under the unwanted incident in both the settled and the unknown incident. A model for signifying derivative incident introduced jointly with an Advanced Sampling Technique that come close to the derived incident probabilities. This augmentation executed the prioritization techniques. In this prioritization techniques, recognize such cases in which the order of incident finding is strong-minded and mechanism for the definition of a settled detection execution.

An intelligent algorithm with interactive learning mechanism for high‐dimensional optimization problem based on improved animal migration optimization

SummaryRecently, a new intelligent algorithm called animal migration optimization (AMO) is proposed. The AMO algorithm, which is inspired by the behavior of animal migration, exhibits good performance on a series of benchmark functions whose dimensionality is less than or equal to 30. However, its performance deteriorates evidently when the dimensionality is more than 30. To overcome this drawback, we proposed an improved AMO (IAMO) with interactive learning mechanism in this article. First, an interactive learning mechanism is designed to improve the intelligence of AMO. During the the search process, individuals learn from each other by the exchange of information, and the search mode is adjusted dynamically. Second, a refined search method is adopted to enhance the search ability of AMO. Individuals will search around the current solutions in order to improve the quality of solutions. Third, a birth‐and‐death strategy is introduced to avoid local optimum. In addition, the property convergence of IAMO is analyzed theoretically. The proposed algorithm is validated on several standard benchmark functions whose dimensionality is 50, 100, and 150, respectively. The empirical results demonstrate that the performance of our proposed algorithm is promising.

Performance Analysis of Animal Migration Optimization Algorithm in Extracting Solar Cell Double Diode Model Parameters

Modeling solar cell involved the formulation of the current versus voltage (I − V ) non-linear curve. Obtaining the accurate model parameters value is important for better performance evaluation, simulation and control of solar cell and module. Extracting these values using traditional methods required more resources, therefore, the used of meta heuristic optimization method become an attractive choice. Some optimization algorithms have been used to estimate the model parameters. However, more investigation is needed to improve model estimation. In this paper, the performance of Animal Migration Optimization (AMO) technique in identifying the unknown parameters of solar cell double diode model is studied. A measurement data of a 57 mm diameter commercial (R.T.C. France) silicon solar cell is used to observe the performance of this algorithm and the consistency of accurately estimating various parameters. The results show that the estimated and experimental data are accurately fitted and certify a good agreement. Furthermore, comparative study among different parameter estimation techniques is presented to demonstrate the effectiveness of the proposed approach.

AMIGM: Animal Migration Inspired Group Mobility Model for Mobile Ad hoc Networks

The most widespread notion of mobility model is the representation of mobile node’s movement pattern in the wireless ad hoc networks which has a significant impact on the performance of the network protocols. In this paper, we have proposed an Animal Migration Inspired Group Mobility (AMIGM) model for mobile ad hoc networks based on the migration behavior of animals like, insects, flock of birds, schools of fishes, reptiles, amphibians, etc. The propound model tries to overcome the limitations of the existing mobility models, such as temporal dependencies, spatial dependencies, geographical restrictions and migration of nodes between the group of nodes so that it can realistically model the real world application scenarios. The proposed AMIGM model is based on Animal Migration Optimization (AMO) algorithm, in which each group of nodes has two phases namely, Migration phase and Population updating phase. In the first phase, the model simulates the movement of nodes in the group from one position to another by obeying the swarming laws. In the second phase, the model simulates joining and leaving of the nodes in the group during migration. The protocol dependent and independent performance metrics of the proposed model are compared with Random Waypoint Mobility model (RWP) and Reference Point Group Mobility model (RPGM) through ns-2 simulator.