Symbiotic Organisms Search Algorithm Research Articles

Fuzzy analytic hierarchy process based generation management for interconnected power system

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

Optimizing beamforming in IoT-WET: A symbiotic-based approach under imperfect channel state information

This study delves into the energy optimization problem in low-power Internet of Things (IoT) networks utilizing wireless energy transfer (WET) technology. In such IoT systems, conventional optimization schemes relying on complete knowledge of channel state information (CSI) are often irrelevant for practical implementation due to their high complexity and energy consumption. To address this challenge and enhance the fairness of energy harvesting among IoT devices with imperfect CSI, we propose a novel beamforming design based on the symbiotic organisms search (SOS) algorithm. Through comprehensive numerical analysis, we demonstrate the effectiveness of our proposed scheme in terms of computational complexity, convergence speed, and energy harvested at each IoT device. The results highlight the significant advantages of our approach compared to conventional methods, paving the way for practical and efficient energy optimization in IoT networks.

A Novel Fractional Order Proportional Integral-Fractional Order Proportional Derivative Controller Design Based on Symbiotic Organisms Search Algorithm for Speed Control of a Direct Current Motor

A Novel Fractional Order Proportional Integral-Fractional Order Proportional Derivative Controller Design Based on Symbiotic Organisms Search Algorithm for Speed Control of a Direct Current Motor

Sequence planning for on-orbit robotic assembly based on symbiotic organisms search with diversification strategy

The demands for efficiency and stability in space missions have necessitated the development of sequence planning schemes for on-orbit robotic assembly of space structures. A sequence planning method is proposed in this work based on robotic kinematic analysis and Symbiotic Organisms Search (SOS) algorithm with a diversification strategy. With feasibility as the constraint and assembly cost as the objective function, the assembly sequence planning is regarded as an optimization problem. The assembly relationship constraint is depicted as the dependency between assembly steps represented by a directed acyclic graph. An abstract modeling method based on robotic kinematics is proposed to construct geometric feasibility constraint, accommodating the interference stemming from the multi-degree-of-freedom motion of the robotic system. A diversification strategy is exploited to address the issue of local optima arising from solving the optimal sequence based on SOS. A cost prediction method using previously calculated populations as samples is proposed to reduce the reliance on robotic dynamic analysis for cost calculation. Finally, simulations are carried out to validate the practicability of the proposed method.

Application of modified symbiotic organisms search (msos) algorithm hybrid with Bayesian optimization leveling (bol) algorithm to propose optimal resource cost options

Application of modified symbiotic organisms search (msos) algorithm hybrid with Bayesian optimization leveling (bol) algorithm to propose optimal resource cost options

On the optimal synthesis of sparsely thinned planar array antenna with constraints using meta‐heuristic optimization

SummaryThe investigations related to the planar antenna array have attracted much attention due to their vast applications in the areas of advanced wireless communication and electromagnetics. This article presents an effective synthesis method of a sparsely thinned symmetric planar antenna array using three well‐known meta‐heuristics including symbiotic organism search (SOS) algorithm, moth fly optimization (MFO), and multi‐verse optimization (MVO) algorithms. The main aim of this work is to optimize the positions of the switched‐on antenna elements on the array aperture in order to reduce the value of side‐lobe levels in the radiation field pattern in multiple planes for a desired first null beam width and subsequently to obtain the maximum reduced number of array‐elements in the antenna array. Two different cases are performed to optimize the radiation pattern in different azimuth angles with two different examples. The proposed methods can constrain the total number of array elements, inter‐element distance, and aperture area of the array. The radiation pattern characteristic and computation time linked with each example and each algorithm are recorded and compared with each other as well as with a fully populated planar symmetric rectangular array antenna of same aperture size for arriving at the conclusion. The simulation‐based results demonstrate the effectiveness of the proposed design and the efficiency of the performance using the SOS algorithm.

A solution to multi Objective Stochastic Optimal Power Flow problem using mutualism and elite strategy based Pelican Optimization Algorithm

Wind Energy is rapidly growing into one of the most popular energy options for power generation. However, the unpredictability of wind necessitates the employment of a distribution function when describing the wind environment. In the depiction of wind speeds, the two significant parameters of the Weibull distribution, such as the scale parameter and the shape parameter are often utilized. Therefore, it is essential to choose the most appropriate approach for estimating these parameters. Furthermore, the uncertainty in delegated power from variable wind speeds makes system management challenging. In this paper, the Weibull distribution parameters were determined using the Pelican Optimization Algorithm (POA). POA has gained extensive popularity among the research community and is currently used to address a wide range of optimization problems. However, the exploration and exploitation are not properly balanced in this algorithm. To overcome this problem a novel hybrid algorithm namely Mutualism phase based POA (MPOA) is proposed with the help of mutualism phase of Symbiotic Organisms Search (SOS) algorithm. To demonstrate the efficacy and robustness MPOA is applied on multi-objective Optimal Power Flow (MO-OPF) problems. For this study IEEE 30 bus standard system and modified IEEE 30 bus system consisting of two wind farms are taken. The purpose of this work is to examine the impact of changing the Weibull parameters, reverse and penalty cost coefficients on the overall generation cost while considering all the security aspects.

Enhancing flood prediction in Southern West Bengal, India using ensemble machine learning models optimized with symbiotic organisms search algorithm

Enhancing flood prediction in Southern West Bengal, India using ensemble machine learning models optimized with symbiotic organisms search algorithm

A systematic review of symbiotic organisms search algorithm for data clustering and predictive analysis

Abstract In recent years, the field of data analytics has witnessed a surge in innovative techniques to handle the ever-increasing volume and complexity of data. Among these, nature-inspired algorithms have gained significant attention due to their ability to efficiently mimic natural processes and solve intricate problems. One such algorithm, the symbiotic organisms search (SOS) Algorithm, has emerged as a promising approach for clustering and predictive analytics tasks, drawing inspiration from the symbiotic relationships observed in biological ecosystems. Metaheuristics such as the SOS have been frequently employed in clustering to discover suitable solutions for complicated issues. Despite the numerous research works on clustering and SOS-based predictive techniques, there have been minimal secondary investigations in the field. The aim of this study is to fill this gap by performing a systematic literature review (SLR) on SOS-based clustering models focusing on various aspects, including the adopted clustering approach, feature selection approach, and hybridized algorithms combining K-means algorithm with different SOS algorithms. This review aims to guide researchers to better understand the issues and challenges in this area. The study assesses the unique articles published in journals and conferences over the last ten years (2014–2023). After the abstract and full-text eligibility analysis, a limited number of articles were considered for this SLR. The findings show that various SOS methods were adapted as clustering and feature selection methods in which CSOS, discrete SOS, and multiagent SOS are mostly used for the clustering applications, and binary SOS, binary SOS with S-shaped transfer functions, and BSOSVT are used for feature selection problems. The findings also revealed that, of all the selected studies for this review, only a few studies specifically focused on hybridizing SOS with K-means algorithm for automatic data clustering application. Finally, the study analyzes the study gaps and the research prospects for SOS-based clustering methods.

A local–global optimization approach for maximizing the multiphysics frequency response of laminated functionally graded CNTs reinforced magneto-electro-elastic plates

Smart materials have garnered significant attention due to their distinct properties and potential applications in various engineering fields. In line with this, this study presents a local–global optimization approach aimed at maximizing the multiphysics frequency of laminated functionally graded carbon nanotube reinforced magneto-electro-elastic (FG-CNTMEE) plates. The laminated FG-CNTMEE plate is composed of layers of functionally graded carbon nanotubes embedded in an electro-elastic matrix, rendering it capable of exhibiting exceptional properties in response to magnetic and electric fields. To optimize its performance, a Balance Enhanced Symbiotic Organisms Search (BESOS) algorithm with new modifications of phases is proposed for global optimization, and integrated with a multiphysical isogeometric analysis using refined zigzag theory (MIARZ) for precise local–global analysis of laminated FG-CNTMEE plates within a Computer-Aided Design (CAD) environment. The effectiveness, robustness, and reliability of the proposed approach are demonstrated through numerical examples that highlight its applicability in achieving optimal designs for smart laminated FG-CNTMEE plates.

An Improved Heterogeneous Comprehensive Learning Symbiotic Organism Search for Optimization Problems

The Symbiotic Organism Search (SOS) algorithm, and most of its variants, exhibit the same search behaviour. That is, each organism or individual in the ecosystem applies the same update rules according to 3 phases: mutualism, commensalism, and parasitism. By applying this strategy, an organism is capable of crippling the search effort of another organism. To overcome this problem, this paper proposes a Heterogeneous Comprehensive Learning SOS (HCLSOS) which divides the population into two distinct subpopulations namely: the exploration and exploitation subpopulations. HCLSOS allows each organism to follow one of two search behaviour according to into its subpopulation: whether to explore or exploit. This addresses the problem of balancing exploration and exploitation in SOS. The proposed algorithm employs a comprehensive learning strategy for the exploration group to generate a new type of mutual vector called the multispecies mutual vector capable of preserving organisms' diversity and discouraging premature convergence. Information exchange between the two groups is unidirectional and managed through a random elite learning strategy. Through this collaboration, HCLSOS can effectively evolve organisms to explore the search space and properly exploit the discovered optimal regions. The study tested HCLSOS on 23 benchmark functions, CEC2014, CEC2017, and the recent CEC 2022 test suites. The outcome of HCLSOS is compared with 15 state-of-the-art algorithms, and the results obtained showed that HCLSOS can attain competitive or even better results. Furthermore, we applied HCLSOS to solve 3 constrained engineering problems and to design an efficient frequency control of a two-area islanded microgrid system.

FACTS placement for reactive power planning with weak node constraints using an improved symbiotic search algorithm

In this paper, an economically feasible and reliable operation of the IEEE 57 bus system for Optimal Power Flow (OPF) is proposed. The Improved Symbiotic Organisms Search (ISOS) algorithm is proposed for effective reactive power planning as an OPF issue. Further, the optimal position of Flexible AC Transmission Systems (FACTS) is taken into consideration, by including the existing system controlling variables like reactive power generators output, transformer tapping and capacitors connected at shunt. The objective of the work is two-fold; i.e., to reduce the energy loss and to enhance the voltage profile within the prescribed limit by ensuring the economic operation and investment cost of FACTS in the system. In this work, two FACTS devices like Static Var Compensator (SVC) and Thyristor-Controlled Series Controller (TCSC) have been taken into consideration. Voltage sensitivity indicator and reactive power flow are two tools that are utilised in order to locate weak nodes for the implementation of FACTS. Finally, the performance of the ISOS algorithm is compared with that of three other state- of-the -art optimization techniques, such as, Symbiotic Organisms Search (SOS), Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO). A Non-parametric statistical analysis is also performed to investigate the dominance of the ISOS algorithm over others.

A novel solution for the optimal reactive power dispatch problem using an artificial neural network integrated with the firefly optimization algorithm

This article applies a novel intelligence technique to solve power system issues faced daily. Compensation for reactive power is a significant issue faced by power system operators in research. The solution can be obtained by handling a multi-objective task and multiconstraints by reducing the active power loss and minimizing the voltage deviation at the load end. The novelty of the research focuses on integrating artificial neural network techniques with the firefly algorithm, a novel optimization algorithm for attaining an objective function. The Levenberg–Marquardt back-propagation algorithm is most suited for proper tuning of the control variables. The objective of this research can be attained by appropriately tuning the control variables connected with the IEEE test bus systems, which helps to maximally improve the voltage profile. Existing research studies have focused on reactive power management, which is attained by solving optimal reactive power flow problems employing nature-inspired approach techniques such as the symbiotic organism search algorithm, the cuckoo search algorithm, the black hole algorithm, the krill herd algorithm, and whale optimization. The evolving strategy, the firefly algorithm (FFA), minimizes the multiconstraint functions more competently and effectively than any conventional algorithm. To showcase the strength of the firefly algorithm incorporating AI, it is examined on standard IEEE test bus systems, namely, the 14-, 30-, and 58-bus networks. The obtained results quantify the effectiveness of the proposed methodology, that is, the artificial intelligence technique implementing the firefly algorithm gives better results than conventional methods.

Combined heat and power economic emission dispatch using dynamic switched crowding based multi-objective symbiotic organism search algorithm

Combined heat and power economic emission dispatch (CHPEED) problem is a highly complex, non-linear, non-convex multi-objective optimization problem due to two conflicting objectives and various operational constraints such as valve-point loading effect, power transmission loss, prohibited operating zone, and the feasible operating region of combined heat and power unit. In order to overcome these challenges, it is necessary to design an algorithm that exhibits a search behavior, which is suitable for the characteristics of objective and constraint space of the CHPEED problem. For these reasons, a dynamic switched crowding based multi-objective symbiotic organism search (DSC-MOSOS) algorithm was designed to meet the requirements and geometric space of the CHPEED problem. By applying the DSC method in the MOSOS algorithm, it was aimed to improve the exploration ability, to strengthen exploitation-exploration balance, and to prevent the catching into local solution traps. A comprehensive experimental study was carried out to prove the performance of the proposed algorithm on IEEE CEC 2020 multi-modal multi-objective problems (MMOPs) and CHPEED problem. In the experimental study conducted among eleven versions of MOSOS variations created with DSC-method and the base MOSOS algorithm on IEEE CEC 2020 MMOPs, according to Friedman scores based on the four performance metrics, the base MOSOS algorithm ranked the last. In other experimental study, the best DSC-MOSOS variant was applied to solve the CHPEED problem, where 5-, 7-, 10- and 14-unit test systems and eight case studies were considered. The important points of this study were that 10-unit and 14-unit test systems were presented to the literature, and the prohibited operating zone was considered in CHPEED problem for the first time. According to the results obtained from eight case studies obtained from the DSC-MOSOS and fourteen competitor algorithms, while the improvement in cost was between 0.2% and 16.55%, the reduction of the emission value was between 0.2 kg and 42.97 kg compared to the competitor algorithms. On the other hand, the stability of the DSC-MOSOS and the base MOSOS was evaluated using stability analysis. While the MOSOS algorithms was not able to perform a success in any case study, the DSC-MOSOS was achieved an average success rate with 91.16%. Thus, the performance of the DSC-MOSOS over the MOSOS was verified by the results of experimental studies and analysis.

A new decomposition-based multi-objective symbiotic organism search algorithm for solving truss optimization problems

This study presents a comprehensive study on the potential and efficacy of decomposition-based multi-objective symbiotic organism search (MOSOS/D) algorithm for truss structure optimization. The investigation is carried out on five benchmark truss structures, e.g., 37-bar, 60-bar, 72-bar, 120-bar, and 200-bar truss problems. The optimization performance of MOSOS/D is compared with other established algorithms, such as Multi-Objective Evolutionary Algorithm Based on Decomposition, Non-Dominated Sorting Genetic Algorithm-II, Multi-Objective Equilibrium Optimizer, Multi-Objective Marine Predator Algorithm, Decomposition-Based Multi-Objective Heat Transfer Search, Multi-Objective Passing Vehicle Search, Multi-Objective Evolutionary Algorithm Based on Decomposition Differential Evaluation, Multi-Objective Symbiotic Organisms Search, and Multi-Objective Multi-Verse Optimizer considering several metrics, including Generational Distance, Spacing, Spread, Inverted Generational Distance, Hypervolume and Runtime. The findings demonstrate MOSOS/D’s competitiveness in providing non-dominated solutions with less space between them. It also achieves a good balance between convergence and coverage and, thus, delivers diverse solutions with less computational complexity. However, the efficiency of the MOSOS/D algorithm varies depending on the complexity of the considered truss structure, with some algorithms occasionally outperforming it in one or two aspects for specific benchmarks. This study provides valuable insights to engineers and designers aiming to achieve optimal truss structure design.

Robust training of median dendritic artificial neural networks for time series forecasting

Although artificial neural network models have produced very successful results in the time series forecasting problem in recent years, an outlier or outliers in the data set adversely affect the forecasting performance of the artificial neural network models. Dendritic neuron model artificial neural networks which are the most similar neural network model to an artificial neural network model are also adversely affected by outliers in the data set like many artificial neural network models in the literature. In this study, to prevent the dendritic neuron model artificial neural networks from being affected by the outliers in the data set; a robust learning algorithm based on Talwar's m estimator, median statistics to prevent the effect of outliers in the inputs, and a new data pre-processing method are used together in a network structure. In addition, the training of the proposed artificial neural network model is carried out with the symbiotic organism search algorithm. To evaluate the performance of the proposed method, analyses are carried out over the closing prices of the time series of Spain, Italy and German stock exchanges in certain years. According to the results of the analysis of the time series of the relevant stock exchanges, both in their original state and by injecting outliers into the time series, the proposed method has superior forecasting performance even when the time series contains outliers and does not contain outliers.

Optimum Scheduling of a Multi-Machine Flexible Manufacturing System Considering Job and Tool Transfer Times without Tool Delay

In order to minimize makespan (Cmax) without causing tool delay with the fewest copies of each tool type, this study investigates the concurrent scheduling of automated guided vehicles (AGVs), machines (MCs), tool transporter (TT), and tools in a multi-machine flexible manufacturing system (FMS). The tools are housed in a central tool magazine (CTM), accessible to and utilized by several machines. AGVs and the tool transporter (TT) move jobs and tools between machines. Since it involves allocating tool copies and AGVs to job operations, sequencing job operations on machines, and related trip operations, such as AGVs’ and TT’s empty trip and loaded trip times, this simultaneous scheduling problem is highly complicated. This issue is resolved using the symbiotic organisms search algorithm (SOSA), based on the symbiotic interaction strategies that organisms adapt to survive in the ecosystem. This study proposes a mixed nonlinear integer programming formulation to address this problem. Verification is performed using an industrial problem from a manufacturing organization. The results show that employing two copies for two tool types out of 22 tool kinds and one copy for the remaining tool types results in no tool delay, which causes a reduction in the Cmax as well as cost. The industries that can benefit directly from this study are consumer electronics manufacturers, original equipment manufacturers, automobile manufacturers, and textile machine producers. The results demonstrate that the SOSA provides promising results compared to the flower pollination algorithm (FPA).

Application of henry gas solubility optimization algorithm for short-term hydrothermal scheduling considering variable water transportation delay and penstock head loss

This paper deals with the solution of short-term hydrothermal scheduling considering some practical scenario of hydro system like variable water time delay and penstock head loss along with other constraints related to hydro and thermal plant. The problem has been solved using Henry gas solubility optimization algorithm. This algorithm is basically works based on the principle of Henry's law. A test system consists of seven hydro units and eight thermal units have been adopted for study purpose. This test system is mainly inspired from Damodar valley corporation, India. The problem has been solved in MATLAB platform. The simulation results have been compared with symbiotic organism search algorithm, ion motion optimization algorithm, crow search algorithm and sine cosine algorithm. The results suggest that the proposed algorithm outperforms other algorithms in terms of quality solution and computation efficiency.

Multiobjective sparrow search feature selection with sparrow ranking and preference information and its applications for high-dimensional data

To reduce the dimensionality of high-dimensional data and enhance its classification accuracy, feature selection can be regarded as a multiobjective optimization problem that can be addressed by evolutionary computation algorithms with promising results. However, balancing the convergence and diversity of nondominated solutions remains challenging. To address these issues, this paper proposes a multiobjective sparrow search feature selection approach with sparrow ranking and preference information and its applications for high-dimensional data. First, during the population updating process, the updating formula of observers is combined with the mutualism phase of the symbiotic organisms search algorithm to design a location updating formula of observers in the sparrow search algorithm, balancing the local development ability and global search ability of sparrow search algorithm and increasing the possibility of moving closer to the optimal solution. Second, based on the dominant and nondominated sparrow individuals of each sparrow, the dominant and nondominated distances are defined, and the sparrow ranking is proposed. The feature ranking is then designed by using the definition of sparrow ranking. Following the definition of sparrow ranking and feature ranking, the selection strategy and location updating formula of the producers in the sparrow search algorithm are proposed. Finally, a preference information-based mutation algorithm is designed to augment the diversity of the nondominated solutions and more effectively guide the whole sparrow population to a better solution. The experimental results on 14 high-dimensional datasets show that the proposed algorithm outperforms different single-objective and multiobjective optimization feature selection algorithms in terms of classification efficiency and diversity of solutions.

Optimal transmission voltage deviation based on Fractional Kinetic Gas Molecular Optimization Symbiotic Organism Search Algorithm for network operational stability improvement

This research applies Fractional Kinetic Gas Molecular Optimization Symbiotic Organism Search (FKGMOSOS) algorithm to optimize Total Voltage Deviation (TVD) while improving the security of an electricity transmission system. Hybrid KGMOSOS has excellent rate of convergence to optimum, but in order to avoid the possibility of getting stuck in local optimum, Fractional Differential Order (FDO) was integrated into the velocity term in the algebraic model of KGMOSOS leading to FKGMOSOS evolution. Using this algorithm, optimal TVD was accomplished by varying the levels of the grid control state variables to attain the finest set of optimal results in IEEE 30 – Bus, 13 – Variable standard network. The optimization competence of this new technique was verified for multiple runs through comparative investigation with that of Fractional Particle Swarm Optimization Gravitational Search Algorithm (FPSOGSA) for each FDO scenario. The results of simulation showed that for 250 successive iterations, FKGMOSOS outperformed FPSOGSA which also outperformed other literary techniques.