2,901 research outputs found

    Symbiotic Organisms Search Algorithm: theory, recent advances and applications

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    The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

    A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search

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    The increasing numbers of design variables and constraints have made many civil engineering problems significantly more complex and difficult for engineers to resolve in a timely manner. Various optimization models have been developed to address this problem. The present paper introduces Symbiotic Organisms Search (SOS), a new nature-inspired algorithm for solving civil engineering problems. SOS simulates mutualism, commensalism, and parasitism, which are the symbiotic interaction mechanisms that organisms often adopt for survival in the ecosystem. The proposed algorithm is compared with other algorithms recently developed with regard to their respective effectiveness in solving benchmark problems and three civil engineering problems. Simulation results demonstrate that the proposed SOS algorithm is significantly more effective and efficient than the other algorithms tested. The proposed model is a promising tool for assisting civil engineers to make decisions to minimize the expenditure of material and financial resources

    Improved Modified Symbiosis Organisms Search (IMSOS): A New and Adaptive Approach for Determining Model Parameters from Geoelectrical Data

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    Symbiotic Organisms Search (SOS) is a global optimization algorithm inspired by the natural synergy between the organisms in an ecosystem. The interactive behavior among organisms in nature simulated in SOS consists of mutualism, commensalism, and parasitism strategies to find the global optimum solution in the search space. The SOS algorithm does not require a tuning parameter, which is usually used to balance explorative and exploitative search by providing posterior sampling of the model parameters. This paper proposes an improvement of the Modified SOS (MSOS) algorithm, called IMSOS, to enhance exploitation along with exploration strategies via a modified parasitism vector. This improves the search efficiency in finding the global minimum of two multimodal testing functions. Furthermore, the algorithm is proposed for solving inversion problems in geophysics. The performance of IMSOS was tested on the inversion of synthetic and field data sets from self-potential (SP) and vertical electrical sounding (VES) measurements. The IMSOS results were comparable to those of other global optimization algorithms, including the Particle Swarm Optimization, the Differential Evolution and the Black Holes Algorithms. IMSOS accurately determined the model parameters and their uncertainties. It can be adapted and can potentially be used to solve the inversion of other geophysical data as well

    Optimal SVC allocation via symbiotic organisms search for voltage security improvement

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    It is desirable that a power system operation is in a normal operating condition. However, the increase of load demand in a power system has forced the system to operate near to its stability limit whereby an increase in load poses a threat to the power system security. In solving this issue, optimal reactive power support via SVC allocation in a power system has been proposed. In this paper, Symbiotic Organisms Search (SOS) algorithm is implemented to solve for optimal allocation of SVC in the power system. IEEE 26 Bus Reliability Test System is used as the test system. Comparative studies are also conducted concerning Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) techniques based on several case studies. Based on the result, SOS has proven its superiority by producing higher quality solutions compared to PSO and EP. The results of this study can benefit the power system operators in planning for optimal power system operations

    Multiobjective adaptive symbiotic organisms search for truss optimization problems

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    This paper presents a multiobjective adaptive symbiotic organisms search (MOASOS) and its two-archive technique for solving truss optimization problems. The SOS algorithm considers the symbiotic relationship among various species, such as mutualism, commensalism, and parasitism, to live in nature. The heuristic characteristics of the mutualism phase permits the search to jump into not visited sections (named an exploration) and allows a local search of visited sections (named an exploitation) of the search region. As search progresses, a good balance between an exploration and exploitation has a greater impact on the solutions. Thus, adaptive control is now incorporated to propose MOASOS. In addition, two-archive approach is applied in MOASOS to maintain population diversity which is a major issue in multiobjective meta-heuristics. For the design problems, minimization of the truss� mass and maximization of nodal displacement are objectives whereas elemental stress and discrete cross-sectional areas are assumed to be behaviour and side constraints respectively. The usefulness of these methods to solve complex problems is validated by five truss problems (i.e. 10-bar truss, 25-bar truss, 60-bar truss, 72-bar truss, and 942-bar truss) with discrete design variables. The results of the proposed algorithms have demonstrated that adaptive control is able to provide a better and competitive solutions when compared against the previous studies

    Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

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    This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods

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

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    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 weight- ing” (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 simulta- neously 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

    Size, Topology, and Shape Optimization of Truss Structures using Symbiotic Organisms Search

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    Truss structures are common in the building industry. One way to contain construction costs is to implement structural optimization. Optimization has to consider cross-sectional size, area, topology, and node coordinates as design variables. However, each truss structure has numerous complex constraints and variables that make optimizing this structure complex and difficult. The metaheuristic method is efficient and effective in solving large and complex problems. This paper tested three metaheuristic algorithms: particle swarm optimization (PSO), differential evolution (DE), and symbiotic organisms search (SOS). Each algorithm was used to optimize a 10-bar planar truss structure and a 15-bar planar truss structure. SOS was found to have the best optimization results, convergence behavior, and consistency

    Using Biological Knowledge for Layout Optimization of Construction Site Temporary Facilities: A Case Study

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    In recent years, a number of studies have successfully transformed various models for biological collective behavior into intelligent optimization algorithms. These bio-inspired optimization techniques have been developed to provide better solutions than traditional methods to a variety of engineering problems. This paper attempts to apply and compare recent bio-inspired algorithms for determining the best layout of construction temporary facilities. To validate the performance of the proposed techniques, an actual building construction project was used as a test problem. Based on the obtained results, the performance of each bio-inspired algorithm is highlighted and discussed. This paper presents beneficial insights to decision-makers in the construction industry that are involved in handling optimization problems

    A novel fuzzy adaptive teaching–learning‑based optimization (FATLBO) for solving structural optimization problems

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    This paper presents a new optimization algorithm called fuzzy adaptive teaching–learning-based optimization (FATLBO) for solving numerical structural problems. This new algorithm introduces three new mechanisms for increasing the searching capability of teaching–learning-based optimization namely status monitor, fuzzy adaptive teaching–learning strategies, and remedial operator. The performance of FATLBO is compared with well-known optimization methods on 26 unconstrained mathematical problems and five structural engineering design problems. Based on the obtained results, it can be concluded that FATLBO is able to deliver excellence and competitive performance in solving various structural optimization problems
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