Symbiotic Organism Search Optimization Research Articles

Modified symbiotic organisms search optimization for automatic construction of convolutional neural network architectures

Convolutional Neural Networks (ConvNets) have demonstrated impressive capabilities in image classification; however, the manual creation of these models is a labor-intensive and time-consuming endeavor due to their inherent complexity. This research introduces an innovative approach to Convolutional Neural Network (ConvNet) architecture generation through the utilization of the Symbiotic Organism Search ConvNet (SOS_ConvNet) algorithm. Leveraging the Symbiotic Organism Search optimization technique, SOS_ConvNet evolves ConvNet architectures tailored for diverse image classification tasks. The algorithm's distinctive feature lies in its ability to perform non-numeric computations, rendering it adaptable to intricate deep learning problems. To assess the effectiveness of SOS_ConvNet, experiments were conducted on diverse datasets, including MNIST, Fashion-MNIST, CIFAR-10, and the Breast Cancer dataset. Comparative analysis against existing models showcased the superior performance of SOS_ConvNet in terms of accuracy, error rate, and parameter efficiency. Notably, on the MNIST dataset, SOS_ConvNet achieved an impressive 0.31 % error rate, while on Fashion-MNIST, it demonstrated a competitive 6.7 % error rate, coupled with unparalleled parameter efficiency of 0.24 million parameters. The model excelled on CIFAR-10 and BreakHis datasets, yielding accuracies of 82.78 % and 89.12 %, respectively. Remarkably, the algorithm achieves remarkable accuracy while maintaining moderate model size.

Enhanced symbiotic organism search optimization algorithm for plant disease classification

Since it satisfies all prerequisites for the growth of humanity, agriculture is currently regarded as being the most significant sector for civilization. One of the main forms of human energy production is thought to be plants, which also provide nutrients, cures, etc. Any damage or disease brought on by exposure to pathogens, viruses, bacteria, etc., while cultivating plants results in a decline in productivity, making it crucial to prevent such diseases and take the required precautions to avoid them. Accurately identifying such fatal diseases is a crucial first step for both the businesses and farmers. Six different Convolutional Neural Networks (CNNs) that accept plant leaf images as input, along with the Enhanced Symbiotic Organism Search (ESOS) optimization algorithm, have been implemented in our research. We intend to extensively contrast the various models based on accuracy, precision, recall, and F1-score. In the area of image recognition and classification, convolutional neural networks (CNNs), in particular, and deep learning, in general, are developing. The literature contains a variety of CNN designs. The dataset size, the number of classes, the model’s weights, hypermeters, and optimizers are a few examples of the variables that have an impact on a CNN model’s performance. Because of its benefits, transfer learning and fine-tuning a pre-trained model are now very popular. This study examines the impact of six popular CNN models: DenseNet, MobileNet, EfficientNet, VGG19, ResNet and Inception. As a result, DenseNet demonstrates an optimal accuracy rate of 98% when compared to other models.

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations.

This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.

Symbiotic Organisms Search Optimization based Faster RCNN for Secure Data Storage in Cloud

Because of the Internet of Things (IoT) and wearable devices, information obtained is subject to cyber attacks, making intrusion detection an essential component. The increased number of attacks on IoT devices exposes them to ongoing exploitation and data theft. Mirai, denial of Service (DoS), scan, etc are the major types of attacks conducted on IoT. Traditional attack detection algorithms have more disadvantages like lack of adaptability in all scenarios, low local minima computational complexity, etc. Thus developing a method that is suitable for all environments and has good convergence speed is taken as the motivation of this study. This paper presents a novel Symbiotic Organisms Search Algorithm (SOSA) optimized Faster region-based convolutional neural network (FRCNN) for attack detection and data type classification. The data to be stored in the cloud is analyzed whether they have restricted access, confidential, or unclassified. Depending upon the variety of data, the Optimal homomorphic encryption (OHE) approach is provided for security. The key selection process is done by an Elastic Collision Seeker Optimization Algorithm (ECSOA) which offers secure access to the data owner and prevents the data from unauthorized access. The model is optimized using the algorithm which improves the feature extraction performance. The effectiveness of the proposed model in attack detection is evaluated using two datasets namely the CSE-CIC-ID2018 dataset and the IoTID20 dataset. When compared to the existing techniques, the proposed model offers optimal performance in terms of multiclass attack detection.

Ensemble Learning With Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification and Risk Identification of Other Organs on Histopathological Images

Ensemble Learning With Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification and Risk Identification of Other Organs on Histopathological Images

Symbiotic Organisms Search Optimization to Predict Optimal Thread Count for Multi-threaded Applications

Multicore systems have emerged as a cost-effective option for the growing demands for high-performance, low-energy computing. Thread management has long been a source of concern for developers, as overheads associated with it reduce the overall throughput of the multicore processor systems. One of the most complex problems with multicore processors is determining the optimal number of threads for the execution of multithreaded programs. To address this issue, this paper proposes a novel solution based on a modified symbiotic organism search (MSOS) algorithm which is a bio-inspired algorithm used for optimization in various engineering domains. This technique uses mutualism, commensalism and parasitism behaviours seen in organisms for searching the optimal solutions in the available search space. The algorithm is simulated on the NVIDIA DGX Intel-Xeon E5-2698-v4 server with PARSEC 3.0 benchmark suit. The results show that keeping the thread count equal to the number of processors available in the system is not necessarily the best strategy to get maximum speedup when running multithreaded programs. It was also observed that when programs are run with the optimal thread count, the execution time is substantially decreased, resulting in energy savings due to the use of fewer processors than are available in the system.

Constructing continuum models of acoustic metamaterials via the symbiotic organisms search (SOS) optimization

Based on the symbiotic organisms search (SOS) optimization algorithm, a robust strain gradient (SG) continuum model has been proposed to accurately capture the broadband dispersion relations of one-dimensional acoustic metamaterials. Under the continuous assumption, an unavoidable key step is the Taylor expansion of displacements, which directly influences the accuracy of the corresponding continuum theory. When the wavelength becomes comparable to the periodic characteristic size, the coefficients of Taylor expansions need necessary adjustments due to the discreteness of the microstructure. Thus, the continuum theories still face critical challenges in predicting the broadband dispersion feature. This remains widely open so far. In this study, we attempt to adopt the SOS optimization to determine the optimal Taylor expansion coefficients to guarantee the dispersion diagrams causing the minimal error throughout the first Brillouin zone. The robustness of the SOS-based SG continuum model is demonstrated with three benchmark examples, i.e., the monoatomic, diatomic, and mass-in-mass lattices. Such an attempt of constructing continuum models with the help of optimization tools may shed some new light on continuum mechanics of structure media.

Optimized Weight Point ADF using SOS Algorithm

Active dc filter (ADF) has become the most viable alternatives for the compensation of the harmonics in the power system analysis. These filters are capable enough to minimize the total harmonic distortion (THD) and provide compensation towards the power quality issues appearing in the transmission system. A simulated model of a HVDC system is designed in MATLAB and the disturbance is injected in the form of load change and the controller efficacy is checked. This paper basically deals with the operational characteristics of the active filter for specific voltage rating irrespective of load and used to reduce harmonics present in the output voltage of the HVDC converter when cascaded with the inverter. The gains of the ADF are optimized with Symbiotic Organism Search Optimization (SOS) with THD as a constraint.

A novel chaotic symbiotic organisms search optimization in multilevel image segmentation

Multilevel thresholding-based image segmentation plays a vital role in image processing. It significantly impacts many applications, such as remote sensing, pattern recognition, and medical image diagnosis. Premature convergence due to stuck into the local optima is the main challenge of any evolutionary algorithm-based multilevel image thresholding. Most of the evolutionary algorithms use their stochastic property to comprehensively utilize the search space, which strongly influences premature convergence. This paper presents a novel chaotic symbiotic organisms search (CSOS) optimization for multilevel image segmentation that maintains a strategic distance from premature convergence and improves the performance of conventional symbiotic organisms search (SOS) optimization in multilevel image segmentation. We have analyzed the performance of the proposed CSOS using state-of-the-art entropies such as Kapur’s, Tsallis’, Renyi’s, and Masi’s entropy as objective functions. The experiments on standard used color images are presented to establish the practicality of the proposed algorithm. The results show that the CSOS algorithm with Masi’s entropy is more effective and has wide adaptability to the high-dimensional optimization problems than the other recently proposed algorithms considered in this paper.

Vibration-based structural health monitoring using symbiotic organism search based on an improved objective function

Vibration-based structural health monitoring (VSHM) relying on model updating methods has been developed greatly and nowadays, not only serves as a major subset of SHM, but also shapes a special class of optimization problems. The historical course of evolution of this field via different research groups and with different goals and objectives, has resulted in the emergence of multiple objective functions, each appropriate only for certain damage scenarios and incapable of reasonably addressing others. The natural frequency residual (NFR) is an objective function sensitive to the damage intensity, which in the meantime, fails to predict the damage location in the symmetric structures. The total modal assurance criterion (TMAC) is another objective function which is sensitive to the damage position, but fails to estimate of the damage severity in the uniformly damaged structures. The present work successfully provides an innovative solution to unify both aforementioned objectives in a holistic objective function (HOF), through a particular combination of NFR and TMAC. Additionally, the competency of the above HOF for solving the VSHM problems has been investigated and demonstrated using a symbiotic organism search (SOS) optimization algorithm. The robustness and efficiency of the proposed VSHM method are verified through assessment of two un-damped benchmark structures. The obtained results indicate that in all damage scenarios, the HOF predicts with a high precision the damage location and succeeds in the high accuracy estimation of the damage extent. Consequently, the combination of the proposed HOF criterion and the SOS optimization algorithm is recommended as a reliable technique for VSHM.

Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams

This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weighting” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simultaneously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root-mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process.

Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times.

This paper addresses the problem of makespan minimization on unrelated parallel machines with sequence dependent setup times. The symbiotic organisms search (SOS) algorithm is a new and popular global optimization technique that has received wide acceptance in recent years from researchers in continuous and discrete optimization domains. An improved SOS algorithm is developed to solve the parallel machine scheduling problem. Since the standard SOS algorithm was originally developed to solve continuous optimization problems, a new solution representation and decoding procedure is designed to make the SOS algorithm suitable for the unrelated parallel machine scheduling problem (UPMSP). Similarly, to enhance the solution quality of the SOS algorithm, an iterated local search strategy based on combining variable numbers of insertion and swap moves is incorporated into the SOS algorithm. More so, to further improve the SOS optimization speed and performance, the longest processing time first (LPT) rule is used to design a machine assignment heuristic that assigns processing machines to jobs based on the machine dynamic load-balancing mechanism. Subsequently, the machine assignment scheme is incorporated into SOS algorithms and used to solve the UPMSP. The performances of the proposed methods are evaluated by comparing their solutions with other existing techniques from the literature. A number of statistical tests were also conducted to determine the variations in performance for each of the techniques. The experimental results showed that the SOS with LPT (SOS-LPT) heuristic has the best performance compared to other tested method, which is closely followed by SOS algorithm, indicating that the two proposed algorithms’ solution approaches are reasonable and effective for solving large-scale UPMSPs.

Symbiotic Organisms Search Technique for SVC Installation in Voltage Control

<p>Increasing demand experienced by electric utilities in many parts of the world involving developing country is a normal phenomenon. This can be due to the urbanization process of a system network, which may lead to possible voltage decay at the receiving buses if no proper offline study is conducted. Unplanned load increment can push the system to operate closes to its instability point. Various compensation schemes have been popularly invented and proposed in power system operation and planning. This would require offline studies, prior to real system implementation. This paper presents the implementation of Symbiotic Organisms Search (SOS) algorithm for solving optimal static VAr compensator (SVC) installation problem in power transmission systems. In this study, SOS was employed to perform voltage control study in a transmission system under several scenarios via the SVC installation scheme. This realizes the feasibility of SOS applications in addressing the compensating scheme for the voltage control study. Minimum and maximum bound of the voltage at all buses have been considered as the inequality constraints as one of the aspects. A validation process conducted on IEEE 26-Bus RTS realizes the feasibility of SOS in performing compensation scheme without violating system stability. Results obtained from the optimization process demonstrated that the proposed SOS optimization algorithm has successfully reduced the total voltage deviation index and improve the voltage profile in the test system. Comparative studies have been performed with respect to the established evolutionary programming (EP) and artificial immune system (AIS) algorithms, resulting in good agreement and has demonstrated its superiority. Results from this study could be beneficial to the power system community in the planning and operation departments in terms of giving offline information prior to real system implementation of the corresponding power system utility.</p>

Improved adaptive fuzzy backstepping control of a magnetic levitation system based on Symbiotic Organism Search

Magnetic levitation systems have become very important in many applications. Due to their instability and high nonlinearity, such systems pose a challenge to many researchers attempting to design high-performance and robust tracking control. This paper proposes an improved adaptive fuzzy backstepping control for systems with uncertain input nonlinear function (uncertain parameters and structure), and applies it to a magnetic levitation system, which is a typical representative of such systems. An adaptive fuzzy system is used to approximate unknown, partially known or uncertain input nonlinear functions of a magnetic levitation system. An adaptation law is obtained based on Ljapunov analysis in order to guarantee closed-loop stability and good tracking performance. Initial adaptive and control parameters have been initialized with Symbiotic Organism Search optimization algorithm, due to strong non-linearity and instability of the magnetic levitation system. The theoretical background of the proposed control method is verified with a simulation study and implementation on a laboratory experimental application.

Correction: Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

[This corrects the article DOI: 10.1371/journal.pone.0158229.].

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

Symbiotic Organism Search optimization based task scheduling in cloud computing environment

Efficient task scheduling is one of the major steps for effectively harnessing the potential of cloud computing. In cloud computing, a number of tasks may need to be scheduled on different virtual machines in order to minimize makespan and increase system utilization. Task scheduling problem is NP-complete, hence finding an exact solution is intractable especially for large task sizes. This paper presents a Discrete Symbiotic Organism Search (DSOS) algorithm for optimal scheduling of tasks on cloud resources. Symbiotic Organism Search (SOS) is a newly developed metaheuristic optimization technique for solving numerical optimization problems. SOS mimics the symbiotic relationships (mutualism, commensalism, and parasitism) exhibited by organisms in an ecosystem. Simulation results revealed that DSOS outperforms Particle Swarm Optimization (PSO) which is one of the most popular heuristic optimization techniques used for task scheduling problems. DSOS converges faster when the search gets larger which makes it suitable for large-scale scheduling problems. Analysis of the proposed method conducted using t-test showed that DSOS performance is significantly better than that of PSO particularly for large search space.