Animal Migration Optimization Algorithm Research Articles

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.

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.

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.

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 .

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.

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.

An augmented animal migration optimization algorithm using worst solution elimination approach in the backdrop of differential evolution

A new algorithm has been formulated based on the basic animal migration optimization (AMO) algorithm. During the course of this proposed work it was revealed for the first time that AMO algorithm is a true replica of DEGL algorithm and when mathematical analysis was carried out the similarities were brought to notice which was till date not reported. Further during the investigation it was also witnessed that AMO algorithm in its virgin form is capable of delivering a competitive performance when applied to optimization of non-linear functions referring to CEC 2014 test suite. Such successful achievements using basic AMO algorithm inspired the present authors to take it as a challenge for exploring the possibility of improving the conventional AMO algorithm with an objective of providing it with a new shape and build an efficient framework for tackling the handicaps encountered in original AMO algorithm. In fact the incessant quest of modifying the basic algorithm gave birth to a new algorithm known as augmented animal migration optimization algorithm in the backdrop of differential evolution (AAMO-DE). The proposed algorithm even though incorporated the philosophy of Jaya algorithm,it created a memory hierarchy of worst solutions generated in each iteration. And unlike Jaya algorithm where the position update equation employ the current worst solution the proposed one picks up a random worst solution from the archive to achieve better diversity and also drops the global best term which often yields biased solutions. The proposed AAMO-DE algorithm could accomplish highly encouraging results when CEC 2014 test suite problems were subjected to validation check. The performance of the proposed algorithm was truly impressive in comparison with its counterparts comprising of state-of-the-art algorithms. In case of application to real world engineering problems the outcomes were very promising and really proves that the proposed AAMO-DE algorithm is not only a strong contender in the optimization community exhibiting excellent results but is also a potentially robust algorithm and has the ability to converge towards global optima without being trapped in local minima as evident from test examples.

A multilevel image thresholding using the animal migration optimization algorithm

Thresholding is an important and well-known technique that plays a major role in distinguishing the image objects from its background. In the other hand, separating the images into several different regions by determining multiple threshold values is called multilevel image thresholding. The Kapur entropy thresholding and maximum between-class variance (Otsu) have been widely used in image thresholding. However, these methods are computationally expensive and with increase in level numbers computational complexity increase exponentially. To overcome this problem, this paper presents animal migration optimization algorithm for multilevel thresholding. For evaluating the efficiency of proposed method, various benchmark images are used for carrying out the experiments, and obtained results via animal migration optimization algorithm compared with most popular optimization technique such as Particle Swarm Optimization, Genetic and bacterial foraging algorithm. Experimental results figure out that the proposed method provides better result than the other tested algorithms.

RETRACTED ARTICLE: QoS and QoE Enhanced Resource Allocation for Wireless Video Sensor Networks Using Hybrid Optimization Algorithm

Resource allocation has posed a challenging of scarce resources to activities over time. Problems of optimal resource allocation are motivated by questions that arise in project scheduling, production planning, computer control, broadcasting, routing data, maintenance scheduling, and etc. Data transmission in environmental, security, and health monitoring requires both quality of service (QoS) and quality of equipment (QoE) aware network in order to ensure efficient usage of the resources and effective access. In this paper, we propose a resource allocation scheme for wireless video sensor network using hybrid optimization (RAS-HO) algorithm. Firstly, the cluster formation is performed by the modified animal migration optimization algorithm, which enhances the energy consumption. Secondly, an efficient resource allocation is performed by a glowworm swarm optimization based decision making algorithm. Simulation results show that the proposed scheme achieves required resources better than existing schemes in terms of QoS metrics are energy efficient, delay fairness, throughput, and QoE metrics are peak signal to noise ratio, structural similarity.

An elitist approach for solving the traveling salesman problem using an animal migration optimization algorithm

This paper presents an improved version of the animal migration optimization (AMO) algorithm for solving the traveling salesman problem (TSP), which is classified as a combinatorial NP-hard problem. AMO is one of the recent metaheuristic algorithms inspired by the migration behavior of animals and has been efficiently applied to a variety of optimization problems. The algorithm is improved by reconstructing the neighborhood topology of each animal during the migration. This modified algorithm is called the elitist animal migration optimization (ELAMO) algorithm, since elitism is introduced as a way in which the positions of the leaders are considered for the neighborhood scheme. To observe the performance of ELAMO, it is compared with AMO and some efficient algorithms. The experimental results showed that the ELAMO algorithm has improved the solution quality of AMO and has produced effective or even competitive values for the selected TSP data sets.

An Information Entropy-Based Animal Migration Optimization Algorithm for Data Clustering

Data clustering is useful in a wide range of application areas. The Animal Migration Optimization (AMO) algorithm is one of the recently introduced swarm-based algorithms, which has demonstrated good performances for solving numeric optimization problems. In this paper, we presented a modified AMO algorithm with an entropy-based heuristic strategy for data clustering. The main contribution is that we calculate the information entropy of each attribute for a given data set and propose an adaptive strategy that can automatically balance convergence speed and global search efforts according to its entropy in both migration and updating steps. A series of well-known benchmark clustering problems are employed to evaluate the performance of our approach. We compare experimental results with k-means, Artificial Bee Colony (ABC), AMO, and the state-of-the-art algorithms for clustering and show that the proposed AMO algorithm generally performs better than the compared algorithms on the considered clustering problems.

Animal Migration Optimization Algorithm for Constrained Engineering Optimization Problems

Animal Migration Optimization Algorithm for Constrained Engineering Optimization Problems

An Improved Animal Migration Optimization Algorithm for Clustering Analysis

Animal migration optimization (AMO) is one of the most recently introduced algorithms based on the behavior of animal swarm migration. This paper presents an improved AMO algorithm (IAMO), which significantly improves the original AMO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique and it is used in many fields. The well-known method in solving clustering problems isk-means clustering algorithm; however, it highly depends on the initial solution and is easy to fall into local optimum. To improve the defects of thek-means method, this paper used IAMO for the clustering problem and experiment on synthetic and real life data sets. The simulation results show that the algorithm has a better performance than that of thek-means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem.

Animal migration optimization: an optimization algorithm inspired by animal migration behavior

In this paper, we intend to propose a new heuristic optimization method, called animal migration optimization algorithm. This algorithm is inspired by the animal migration behavior, which is a ubiquitous phenomenon that can be found in all major animal groups, such as birds, mammals, fish, reptiles, amphibians, insects, and crustaceans. In our algorithm, there are mainly two processes. In the first process, the algorithm simulates how the groups of animals move from the current position to the new position. During this process, each individual should obey three main rules. In the latter process, the algorithm simulates how some animals leave the group and some join the group during the migration. In order to verify the performance of our approach, 23 benchmark functions are employed. The proposed method has been compared with other well-known heuristic search methods. Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained.