Artificial Algae Algorithm Research Articles

Artificial Algae Algorithm for Clustering of Benchmark Datasets

The best solution found for a problem under specific circumstances is called optimization. Algorithms for optimization can make the best use of the information at their disposal. Numerous optimization algorithms have been created thus far by researchers, and most of these algorithms are based on the characteristics of naturally occurring biological organisms. Optimization algorithms have proven to be highly effective in numerous fields, including finance, engineering, and medical. Apart from these applications, they have also been employed in data mining techniques including clustering and classification. In many different domains, the clustering method is widely applied. Finding the optimum cluster centers is the most crucial step in the clustering process. In this study, the Artificial Algae Algorithm (AAA) is used to perform the clustering procedure by using 12 datasets that were taken from the UCI Machine Learning Repository. For every dataset, the squared distance values between the cluster centers and the data were computed in order to assess the effectiveness of AAA. The study evaluated AAA's performance against that of the ALO, DEA, MFO, PSO, TSA, and WOA algorithms. The clustering performance of AAA on benchmark datasets was measured to be better than the performance of other algorithms.

Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images.

We developed an optimized decision support system for retinal fundus image-based glaucoma screening. We combined computer vision algorithms with a convolutional network for fundus images and applied a faster region-based convolutional neural network (FRCNN) and artificial algae algorithm with support vector machine (AAASVM) classifiers. Optic boundary detection, optic cup, and optic disc segmentations were conducted using TernausNet. Glaucoma screening was performed using the optimized FRCNN. The Softmax layer was replaced with an SVM classifier layer and optimized with an AAA to attain enhanced accuracy. Using three retinal fundus image datasets (G1020, digital retinal images vessel extraction, and high-resolution fundus), we obtained accuracy of 95.11%, 92.87%, and 93.7%, respectively. Framework accuracy was amplified with an adaptive gradient algorithm optimizer FRCNN (AFRCNN), which achieved average accuracy 94.06%, sensitivity 93.353%, and specificity 94.706%. AAASVM obtained average accuracy of 96.52%, which was 3% ahead of the FRCNN classifier. These classifiers had areas under the curve of 0.9, 0.85, and 0.87, respectively. Based on statistical Friedman evaluation, AAASVM was the best glaucoma screening model. Segmented and classified images can be directed to the health care system to assess patients' progress. This computer-aided decision support system will be useful for optometrists.

Comparative analysis of optimization algorithm-based training algorithms for a few shot continual learning

Incremental learning relies on the availability of ample training data for novel classes, a requirement that is often unfeasible in various application scenarios, particularly when new classes are rare groups that are pricey or challenging to attain. The main focus of incremental learning is on the tricky task of continuously learning to classify new classes in incoming data with no erasing knowledge of old classes. The research intends to develop a comparative analysis of optimization algorithms in training few-shot continual learning models to conquer catastrophic forgetting. The presented mechanism integrates various steps: pre-processing and classification. Images are initially pre-processed through contrast enhancement to elevate their quality. Pre-processed outputs are then classified by employing Continually Evolved Classifiers, generated to address a matter of catastrophic forgetting. Furthermore, to further enhance performance, Serial Exponential Sand Cat Swarm optimization algorithm (SE-SCSO) is employed and compared against ten other algorithms, containing Grey Wolf Optimization (GWO) algorithm, Moth flame optimization (MFO), cuckoo Search Optimization Algorithm (CSOA), Elephant Search Algorithm (ESA), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Cat Swarm Optimization (CSO), Fish Swarm Algorithm (FSA), Genetic Bee Colony (GBC) Algorithm, and Particle swarm optimization (PSO). From the experiment results, SE-SCSO had attained the maximum performance with an accuracy of 89.6%, specificity of 86%, precision of 83%, recall of 92.3% and f-measure of 87.4%.

Chaotic Artificial Algae Algorithm for Solving Global Optimization With Real-World Space Trajectory Design Problems

AbstractThe artificial algae algorithm (AAA) is a recently introduced metaheuristic algorithm inspired by the behavior and characteristics of microalgae. Like other metaheuristic algorithms, AAA faces challenges such as local optima and premature convergence. Various strategies to address these issues and enhance the performance of the algorithm have been proposed in the literature. These include levy flight, local search, variable search, intelligent search, multi-agent systems, and quantum behaviors. This paper introduces chaos theory as a strategy to improve AAA's performance. Chaotic maps are utilized to effectively balance exploration and exploitation, prevent premature convergence, and avoid local minima. Ten popular chaotic maps are employed to enhance AAA's performance, resulting in the chaotic artificial algae algorithm (CAAA). CAAA's performance is evaluated on thirty benchmark test functions, including unimodal, multimodal, and fixed dimension problems. The algorithm is also tested on three classical engineering problems and eight space trajectory design problems at the European Space Agency. A statistical analysis using the Friedman and Wilcoxon tests confirms that CAA demonstrates successful performance in optimization problems.

MOAAA/D: a decomposition-based novel algorithm and a structural design application

When real-world engineering challenges are examined adequately, it becomes clear that multi-objective need to be optimized. Many engineering problems have been handled utilizing the decomposition-based optimization approach according to the literature. The performance of multi-objective evolutionary algorithms is highly dependent on the balance of convergence and diversity. Diversity and convergence are not appropriately balanced in the decomposition technique, as they are in many approaches, for real-world problems. A novel Multi-Objective Artificial Algae Algorithm based on Decomposition (MOAAA/D) is proposed in the paper to solve multi-objective structural problems. MOAAA/D is the first multi-objective algorithm that uses the decomposition-based method with the artificial algae algorithm. MOAAA/D, which successfully draws a graph on 24 benchmark functions within the area of two common metrics, also produced promising results in the structural design problem to which it was applied. To facilitate the design of the "rectangular reinforced concrete column" using MOAAA/D, a solution space was derived by optimizing the rebar ratio and the concrete quantity to be employed.

A Novel Crossover based Discrete Artificial Algae Algorithm for Solving Traveling Salesman Problem

The Artificial Algae Algorithm (AAA) is a newly proposed metaheuristic algorithm that is inspired by microalgae behaviors. This algorithm has been proposed for solving continuous optimization problems and achieved good results for the continuous problems. In addition, binary versions of AAA are proposed in the literature. This paper presents a discrete version of AAA, which is named Discrete Artificial Algae Algorithm (DAAA). For discretization of AAA, Crossover operators (one-point and uniform) are used in the processes (helical movement, evolutionary process, and adaptation). In this study, in addition to crossover operators, transformation operators such as swapping, insertion, symmetry, and reversion are also used. DAAA’s performance was analyzed on a well-known discrete optimization problem called the Traveling Salesman Problem (TSP). DAAA was tested on thirty-two Benchmark instances of the TSP. These instances were small-sized, medium-sized, and large-sized. Firstly, the AAA processes (evolutionary process, adaptation, and helical movement) with the combination of nearest neighbor and transformation operators were tested for selected benchmark instances and this testing was called Process Analysis. After this process Analysis the best processes with which to continue were selected, and after this decision comparisons with other algorithms were started. The main comparison is between discrete Social Spider Algorithm (DSSA) and DAAA, and DAAA outperformed DSSA on most of the problems. Further, DAAA’s performance on some of the benchmark instances was compared with some of the well-known algorithms for TSP. In this comparison, DAAA has achieved better results than many other algorithms. Experimental results show that DAAA has the capability of solving discrete optimization problems and outperforming other algorithms.

Artificial algae optimization for Virtual Network Embedding problems in 5G network slicing scenarios

Network slicing enables diverse scenarios within The Fifth Generation of Cellular System (5G) and beyond systems. Mapping virtual networks to their physical counterparts, known as the Virtual Network Embedding (VNE) problem, is challenging with mathematical complexities. While Linear Programming (LP) approaches can address the VNE problem, they may prove impractical due to the execution time required to obtain optimal solutions. Furthermore, meta-heuristic algorithms offer a promising alternative in balancing solution optimality and execution time. In this study, we propose the Artificial Algae Algorithm (AAA) as a solution for the VNE problem in network slicing scenarios specific to 5G. We compare the performance of AAA with other commonly used meta-heuristics, including Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Since the evolutionary process of AAA consistently operates in three random dimensions, irrespective of the problem’s dimensionality, the runtime performance of AAA remains unaffected by the number of virtual nodes, yielding execution times up to ten times faster than DE and PSO when considering 30 nodes. In that scenario, the proposed approach with AAA presented an improvement of more than 60% compared to DE and PSO in an execution time ten times lower.

A nested optimization approach for robot gripper multi-objective optimization problem

Robot Gripper Design Optimization Problem is a well-known multi-objective optimization problem explored by various approaches in previous studies over the last few decades. The continuous parameter z plays an important role in determining a robot gripper's minimum and maximum gripping forces. However, previous studies have utilized a simple loop approach in which the value of z is iteratively increased by a constant value from 0 to 50 to determine the gripping forces of a solution. This approach limits the solution space by treating the continuous z parameter as a discrete variable. To overcome this limitation, we propose a nested optimization approach in which a single-objective optimizer is embedded within the fitness function of the main optimizer to determine the optimal value of z. The proposed approach is evaluated using a single-optimizer Simulated Annealing (SA) nested within multi-objective optimizers such as Multi-Objective Gray Wolf Optimizer (MOGWO), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Artificial Algae Algorithm (MOAAA), and Non-Dominated Sorting Genetic Algorithm (NSGA-II). Experimental results demonstrate that our approach significantly outperforms previous studies and achieves the best-known performance. The findings of the study indicate that this method can present a novel framework for researchers in tackling design optimization challenges, encouraging a reevaluation and possible improvement of conventional techniques.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods.

An In-Depth Analysis of Autism Spectrum Disorder Using Optimized Deep Recurrent Neural Network

Autism spectrum disease is one of the severe neuro developmental disorders that are currently present worldwide (ASD). It is a chronic disorder that has an impact on a person’s behaviour and communication abilities. The world health organization’s 2019 study states that an increasing number of people are being diagnosed with ASD, which poses a risk because it is comparable to high medical expenses. Early detection can significantly lessen the impact. Traditional techniques are costly and time-consuming. This paper offers a Novel Deep Recurrent Neural Network (NDRNN) algorithm for the detection of the level of autism to address the aforementioned problems. The deep recurrent neural network is developed with several hidden recurrent network layers with Long-Short Term Memory (LSTM) units. In this work, Artificial Algae Algorithm (AAA) is used as a feature extraction algorithm, to obtain the best optimal features among the listed feature set. An Intelligent Water Droplet (IWD) algorithm is used for obtaining optimal weight and bias value for the recurrent neural network. The algorithm was evaluated for the dataset obtained by the Indian scale for assessment of autism. Experimental results shows that this proposed model produces the 91% of classification accuracy and 92% of sensitivity and reduces the cost.

Secure cluster-based routing using multi objective-trust centric artificial algae algorithm for wireless sensor network

<p>Nowadays, wireless sensor network (WSN) is developed as a key technology to observe and track applications over a wide range. However, energy consumption and security are considered as important issues in the WSN. In this paper, the multi objective-trust centric artificial algae algorithm (M-TCAAA) is proposed to accomplish a secure broadcasting over the WSN. The proposed M-TCAAA is used to choose the secure cluster head (SCH) as well as routing path, based on the distinct fitness measures such as trust, communication cost, residual energy, and node degree. Hence, the M-TCAAA is used to ensure a secure data transmission while decreasing the energy consumed by the nodes. The performance of the M-TCAAA is analyzed by means of energy consumption, packet delivery ratio (PDR), throughput, end to end delay (EED), normalized routing load (NRL), and network lifetime. The existing researches namely energy aware trust and opportunity-based routing with mobile nodes (ETOR-MN), grey wolf updated whale optimization (GUWO), secure cluster-based routing protocol (SCBRP), secure routing protocol based on multi-objective ant-colony-optimization (SRPMA) and multi objective trust aware hybrid optimization (MOTAHO) are considered for evaluating the M-TCAAA. The PDR of the M-TCAAA for 100 nodes is 99.87%, which is larger than the ETOR-MN, GUWO, SRPMA and MOTAHO.</p>

MAA: multi-objective artificial algae algorithm for workflow scheduling in heterogeneous fog-cloud environment

With the development of current computing technology, workflow applications have become more important in a variety of fields, including research, education, health care, and scientific experimentation. A group of tasks with complicated dependency relationships constitute the workflow applications. It can be difficult to create an acceptable execution sequence while maintaining precedence constraints. Workflow scheduling algorithms (WSA) are gaining more attention from researchers as a real-time concern. Even though a variety of research perspectives have been demonstrated for WSAs, it remains challenging to develop a single coherent algorithm that simultaneously meets a variety of criteria. There is very less research available on WSA in the heterogeneous computing system. Classical scheduling techniques, evolutionary optimisation algorithms, and other methodologies are the available solution to this problem. The workflow scheduling problem is regarded as NP-complete. This problem is constrained by various factors, such as Quality of Service, interdependence between tasks, and user deadlines. In this paper, an efficient meta-heuristic approach named Multi-objective Artificial Algae (MAA) algorithm is presented for scheduling scientific workflows in a hierarchical fog-cloud environment. In the first phase, the algorithm pre-processes scientific workflow and prepares two task lists. In order to speed up execution, bottleneck tasks are executed with high priority. The MAA algorithm is used to schedule tasks in the following stage to reduce execution times, energy consumption and overall costs. In order to effectively use fog resources, the algorithm also utilises the weighted sum-based multi-objective function. The proposed approach is evaluated using five benchmark scientific workflow datasets. To verify the performance, the proposed algorithm's results are compared to those of conventional and specialised WSAs. In comparison to previous methodologies, the average results demonstrate significant improvements of about 43% in execution time, 28% in energy consumption and 10% in total cost without any trade-offs.

Solving constrained engineering design problems with multi-objective artificial algae algorithm

Solving constrained engineering design problems with multi-objective artificial algae algorithm

Solving constrained engineering design problems with multi-objective artificial algae algorithm

Solving constrained engineering design problems with multi-objective artificial algae algorithm

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Malware is a ‘malicious software program that performs multiple cyberattacks on the Internet, involving fraud, scams, nation-state cyberwar, and cybercrime. Such malicious software programs come under different classifications, namely Trojans, viruses, spyware, worms, ransomware, Rootkit, botnet malware, etc. Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it; then, the user pays a ransom procedure of a sum of money. To prevent detection, various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning (ML) algorithm. This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection (LBAAA-OMLMD) approach in Computer Networks. The presented LBAAA-OMLMD model mainly aims to detect and classify the existence of ransomware and goodware in the network. To accomplish this, the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection (LBAAA-FS) model to reduce the curse of dimensionality problems. Besides, the Flower Pollination Algorithm (FPA) with Echo State Network (ESN) Classification model is applied. The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results. The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset, and the outcomes are inspected in distinct measures. The comprehensive comparative examination demonstrated the betterment of the LBAAA-OMLMD model over recent algorithms.

Hierarchical Approaches to Solve Optimization Problems

Optimization is the operation of finding the most appropriate solution for a particular problem or set of problems. In the literature, there are many population-based optimization algorithms for solving optimization problems. Each of these algorithms has different characteristics. Although optimization algorithms give optimum results on some problems, they become insufficient to give optimum results as the problem gets harder and more complex. Many studies have been carried out to improve optimization algorithms to overcome these difficulties in recent years. In this study, six well-known population-based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA, and particle swarm optimization - PSO) were used. Each of these algorithms has its own advantages and disadvantages. These population-based six algorithms were tested on CEC’17 test functions and their performances were examined and so the characteristics of the algorithms were determined. Based on these results, hierarchical approaches have been proposed in order to combine the advantages of algorithms and achieve better results. The hierarchical approach refers to the successful operation of algorithms. In this study, eight approaches were proposed, and performance evaluations of these structures were made on CEC’17 test functions. When the experimental results are examined, it is concluded that some hierarchical approaches can be applied, and some hierarchical approaches surpass the base states of the algorithms.

Discrete Artificial Algae Algorithm for solving Job-Shop Scheduling Problems

The Job-Shop Scheduling Problem (JSSP) is an NP-hard problem and can be solved with both exact methods and heuristic algorithms. When the dimensionality is increased, exact methods cannot produce proper solutions, but heuristic algorithms can produce optimal or near-optimal results for high-dimensional JSSPs in a reasonable time. In this work, novel versions of the Artificial Algae Algorithm (AAA) have been proposed to solve discrete optimization problems. Three encoding schemes (Random-Key (RK), Smallest Position Value (SPV), and Ranked-Over Value (ROV) Encoding Schemes) were integrated with AAA to solve JSSPs. In addition, the comparison of these three encoding schemes was carried out for the first time in this study. In the experiments, 48 JSSP problems that have 36 to 300 dimensions were solved with 24 different approaches obtained by integrating 3 different coding schemes into 8 state-of-the-art algorithms. As a result of the comparative and detailed analysis, the best results in terms of makespan value were obtained by integrating the SPV coding scheme into the AAA method.

Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification

Website phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user by offering the masked webpage as legitimate or reliable for retrieving its important information. Presently, anti-phishing approaches necessitate experts to extract phishing site features and utilize third-party services for phishing website detection. These techniques have some drawbacks, as the requirement of experts for extracting phishing features is time consuming. Many solutions for phishing websites attack have been presented, such as blacklist or whitelist, heuristics, and machine learning (ML) based approaches, which face difficulty in accomplishing effectual recognition performance due to the continual improvements of phishing technologies. Therefore, this study presents an optimal deep autoencoder network based website phishing detection and classification (ODAE-WPDC) model. The proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. Then, feature extraction and artificial algae algorithm (AAA) based feature selection (FS) are utilized. The DAE model with the received features carried out the classification process, and the parameter tuning of the DAE technique was performed using the invasive weed optimization (IWO) algorithm to accomplish enhanced performance. The performance validation of the ODAE-WPDC technique was tested using the Phishing URL dataset from the Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model with maximum accuracy of 99.28%.

Power loss minimization with simultaneous location and sizing of distribution generation units using artificial algae algorithm

Power loss is one <span lang="EN-US">of the important pointers used to measure the performance of distributions networks. Many optimization algorithms have been proposed to solve various optimal power flow problems in Electrical Engineering. In this paper, a novel technique, artificial algae algorithm is developed to robustly detect the optimal location and size of distributed generation (DG) units for minimization of total power losses without violating the equality and inequality constraints. The main objective of optimal power flow (OPF) is to maximize or minimize the objective function using various constraint so that steady-state operation point is achieved. The concept of optimal power flow in power system helps to minimize real power loss. In the proposed approach, various control variables like generator bus, voltage magnitudes, and transformer tap settings are considered. The proposed algorithm is simulated in MATLAB and effectiveness is carried on IEEE 33 bus radial distribution system and satisfactory results are achieved when compared with other optimization techniques. A notable improvement in reduction of active power losses with 3 DG operating at different power factors are 65.5%, 42.4%, and 77.8% respectively, were achieved in comparison to the system without DGs and as compared with other research papers.</span>

A multimodal deep learning infused with artificial algae algorithm – An architecture of advanced E-health system for cancer prognosis prediction

Cancer becomes life-threatening when it affects vital organs and their potential to function; therefore, early discovery is crucial for prognosis management. Medical research benefits from the rapid growth of Artificial Intelligence (AI) technology, notably machine learning (ML), deep learning (DL), and reinforcement learning (RL) which has resulted in the development of several approaches and techniques for improving cancer prediction and survival analysis. To improve cancer prognosis prediction and survival analysis, a novel framework that fuses multimodal features via early and late fusion techniques using DL and RL is presented. The approach utilizes an artificial algae algorithm (AAA) to extract the most resilient features from the dataset and build a robust model for cancer prognosis and prediction. The uniqueness of this technique is illustrated by using AAA to extract valuable features from a variety of data modalities infused with Double DEEP Q-NETWORK (DDQN), Convolution eXtreme Gradient Boosting (CNN-XGBOOST), and Convolution Support Vector Machine (CNN-SVM) models for cancer prognostic prediction. The effectiveness of the proposed framework was evaluated using publicly-available datasets: Lower Grade Glioma in the Brain(BRAIN-TCGA), Prostate Cancer, Bladder Cancer, Metastatic Colorectal Cancer (MSKCC), and the METABRIC dataset. The optimum accuracy achieved was 0.99 for the BRAIN-TCGA dataset, surpassing a variety of state-of-the-art techniques. The performance assessment results reveal that the proposed model outperforms currently available cancer prognostic prediction models in terms of predictive performance.