A modified controller design based on symbiotic organisms search optimization for desalination system

The symbiotic organisms search (SOS) algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms. Highlights Correlation between organisms, optimization and engineering. Adaptive symbiotic organisms search (SOS) algorithm is proposed. Implementation on structural design problems. Effective over other methods.

In this study, kinetics of eggplant drying was modeled in the laboratory-scaled Food Drying Oven (FDO) with resistance heater was designed and manufactured. The temperature, energy consumption and drying time of FDO were recorded by keeping the temperature of at different temperatures as 40, 50 and 60 °C. These saved values were chosen as the input parameters of the model. The weight value of the eggplant was taken as the output parameter. Linear and quadratic equations were developed for modeling and constant coefficients of these equations were estimated with Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), symbiotic organisms search (SOS) algorithms. In addition, the performances of these models were compared with the model developed with ANN in terms of performance and time. The results show that the lowest error of the developed linear and quadratic equations was obtained with SOS algorithm. The MSE metric results of ANN were fifty times higher than the performance of SOS algorithm, and the SOS algorithm reached best value three times faster than the ANN.

Symbiotic Organisms Search Algorithm for multilevel thresholding of images

In VLSI physical design vicinity, floorplanning is a crucial and powerful step for fixing circuit layout complexity that's getting extended because of a wider variety of additives are incorporated right into a single chip. Floorplanning provides a ground work to solve this problem by identifying the relative locations of modules(blocks) also estimates dead space (white space), total layout area (chip area) and wirelength among modules. This work describes a multi objective adaptive symbiotic organisms search (SOS) algorithm for soft modules with fixed outline. An adaptivity in Multi Objective Optimization (MOO) leads the way to metaheuristics for solving most of the real time problems be connected with electronics. A novel B*tree crossover operator is used by this SOS floorplanner. In traditional symbiotic organism’s search (SOS) optimization approach, crossover operation over B*tree is not attempted. The proposed SOS algorithm produces the effective combinations of B*tree structures as a result of crossover. Three phases of symbiotic organism’s search namely, mutualism, commensalism, and parasitism are effectively handled by this new B*tree structures. Search space exploration and exploitation are balanced by the effective combinations of B*tree structures. SOS algorithm results are compared with existing optimization methods mentioned in literature. The proposed SOS algorithm is more efficient in area and wirelength minimization than the state-of-the-art algorithms. MCNC (Microelectronics Center of North Carolina) benchmarks are used for testing SOS algorithm. Test results of SOS algorithm proves that better results are produced for wirelength minimization, area minimization and dead space minimization over previous floorplanning algorithms.

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.

DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization

This paper presents symbiotic organisms search (SOS) algorithm for solving bid based economic load dispatch (BBELD) problem in deregulated electricity market. Power generating companies and customers submit their bids to the Independent System Operator. Independent System Operator matches the bids and conduct dispatch depending on the price and MW bidding to maximize the social profit. SOS algorithm minimizes the generator cost while satisfying various load demands, so that the social profit which is the difference between all customers benefit and all generators cost, increases. The IEEE-30 bus system has taken with six generators, two customers and two dispatch periods under low, medium and high bidding strategies. The results of SOS are compared with differential evolution (DE) and particle swarm optimization (PSO) results and social profit obtained by SOS is found to be better than DE and PSO which shows the effectiveness of SOS for solving BBELD problem.

In this contribution, the control of multivariable reverse osmosis (RO) desalination plant using proportional-integral-derivative (PID) controllers is presented. First, feed-forward compensators are designed using simplified decoupling method and then the PID controllers are tuned for flux (flow-rate) and conductivity (salinity). The tuning of PID controllers is accomplished by minimization of the integral of squared error (ISE). The ISEs are minimized using a recently proposed algorithm named as teacher-learner-based-optimization (TLBO). TLBO algorithm is used due to being simple and being free from algorithm-specific parameters. A comparative analysis is carried out to prove the supremacy of TLBO algorithm over other state-of-art algorithms like particle swarm optimization (PSO), artificial bee colony (ABC) and differential evolution (DE). The simulation results and comparisons show that the purposed method performs better in terms of performance and can successfully be applied for tuning of PID controllers for RO desalination plants.

This data article explains the time-series data for optimal operation of Safarud Reservoir located in Halilrood basin in the south of Iran for a period of 223 months, from October 2000 to April 2019. The utilized data included the release of the reservoir, reservoir inflow, reservoir storage, evaporation and precipitation. A model based on Symbiotic Organisms Search (SOS) algorithm was also developed for the optimal operation of Safarud Reservoir. The analysis of the objective function showed that the best solution achieved by the SOS algorithm was 10.89. Also, the analysis of these datasets revealed that the SOS algorithm was efficient for the optimal operation of the reservoir problem.

Symbiotic Organism Search (SOS) algorithm is highly praised by researchers for its excellent convergence performance, global optimization ability and simplicity in solving various continuous practical problems. However, in the real world, there are many binary problems, which can only take values of 0 and 1, that still need to be solved. Since the original SOS algorithm cannot directly solve the binary problem, the original ASOS Binary SOS (BSOS) algorithm has the disadvantage of premature convergence. In order to improve the limitations of the ASBSOS algorithm, we propose an Improved BSOS (IBSOS) algorithm. As we all know, the transfer function is very important in the binarization of continuous optimization algorithms. Therefore, we used 9 transfer functions in the IBSOS algorithm to binarize the continuous SOS algorithm and analyzed the impact of each transfer function on the performance of the BSOS algorithm. Moreover, we use the same three biological symbiosis strategies as the continuous SOS algorithm in our proposed IBSOS algorithm to binarize the SOS algorithm to improve The diversity of the algorithm execution process and the ability to balance algorithm exploration and development. In order to verify the performance of IBSOS using different transfer functions, we use 13 benchmark functions to show the global optimization capability and convergence speed of the BSOS algorithm. Finally, we apply the algorithm to feature selection in the ten data sets of UCI. The experimental results with low classification error and few features further verify the excellent performance of the IBSOS algorithm.

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA). Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value. Projected HGPSOS algorithm improves the quality of the search. Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.

ABSTRACTTransmission expansion planning (TEP) has become a complex problem in restructured electricity markets. This article presents the symbiotic organisms search (SOS) algorithm, a novel metaheuristic optimization technique for solving TEP problems in power systems. The SOS algorithm is inspired by the interactions among organisms in an ecosystem. The TEP problem is formulated here as an optimization problem to determine the cost-effective expansion planning of electrical power systems. Several constraints, such as power flow of the lines, right-of-way validity and maximum line addition, are taken into consideration. First, the SOS algorithm is tested with several benchmark functions. Then, it is applied on three standard power system networks (IEEE 24-bus system, Brazilian 46-bus system and Brazilian 87-bus system) in a TEP study to demonstrate the optimization capability of the proposed SOS algorithm. The results are compared with those produced by other state-of-the-art algorithms.

In this work, proportional-integral-derivative-acceleration (PIDA) controller is designed by using Symbiotic Organisms Search (SOS) algorithm and its robustness against the input disturbance is investigated. In the study, SOS algorithm and performance evaluation of control system are carried out by means of simulation models that are implemented in Matlab/Simulink environment. In this simulation environment, mean square error function of the system is minimized by SOS algorithm to tune PIDA controller coefficients. The PIDA controller is compared with a control system presented in literature and robustness of the PIDA against the disturbance are shown.

Transmission expansion planning (TEP) is a conventional problem of electric power systems. The main objective of TEP is to govern the optimum expansion plan of the electrical power networks. This chapter proposes symbiotic organisms search (SOS) algorithm (a novel metaheuristic optimization technique) for the solution of TEP problem of power systems. SOS algorithm is motivated by the interactions among the organisms in the ecosystem. Both static and dynamic TEP problem have been modeled in this chapter using DC power flow model and are effectively solved by the SOS algorithm. Several constraints such as right-of-way's validity, maximum number of lines addition, power flow of the network lines have been taken into consideration. To authenticate the capability of the proposed method, Garver's 6-bus system, IEEE 25-bus system and Colombian 93-bus system are tested for TEP problem. The efficacy of the proposed SOS algorithm, while dealing with different case studies of the studied power networks, is established in terms of higher quality results (i.e., lower investment cost), lower competitive computational burden and quicker (also stable) convergence mobility.