Evaluation of Flood Routing Models and Their Relationship to The Hydraulic Properties of The Diyala River Bed

In this study, four types of flood routing approaches were studied which give significantly varied results represented by the differences between computed and observed flows and also differ considerably on the friction coefficient and bed slope of the channels. First two approaches use a hydraulic solution to solve the equations of unsteady flow, while the third approach uses the hydrological solution, and the fourth algorithm solves Muskingum approach with seven parameters. All these approaches were run with the same input parameters, the results were compared and tested with four Error Measurement Indices, Sum of Squared Deviations, Error of Peak Discharge, Variance Index, and agreement index. Diyala River was selected for this application. Dynamic wave method gave accurate results, followed by the characteristic method, and then the linear Muskingum-Cunge method, but Symbiotic Organisms Search Algorithm not gave any senses due to change in roughness or bed slope and gave very identical values with recorded outflow in all conditions, which means that the hydraulic solution is better compared to the hydrological solution. The results also showed that the difference between the calculated and observed flows diminished with a decrease in the coefficient of friction and an increase in the bed slope channel.

Enhanced Symbiotic Organisms Search and Hydrological Cycle Algorithms for Real Power Loss Diminution and Voltage Stability Enhancement

In this chapter, enhanced symbiotic organisms search (ESOS) algorithm and hydrological cycle (HC) algorithm are projected to solve factual power loss lessening problem. Symbiotic search algorithm is based on the actions between two different organisms in the ecosystem: mutualism, commensalism, and parasitism. Exploration procedure has been initiated arbitrarily, and each organism indicates a solution with fitness value. Quasi-oppositional-based learning and chaotic local search have been applied to augment the performance of the algorithm. In this work, hydrological cycle (HC) algorithm has been utilized to solve the optimal reactive power problem. It imitates the circulation of water form land to sky and vice versa. Only definite number of water droplets is chosen for evaporation, and it is done through roulette-wheel selection method. In the condensation stage, water drops move closer, combine, and also collusion occurs as the temperature decreases.

An optimal production plan for cashew nuts community enterprise using metaheuristic algorithms

The proper production plan plays an important role in the cashew nuts market enterprise in order to reduce cost. This study aims to find the optimal production plan for cashew nuts using ant lion optimization (ALO), symbiotic organisms search (SOS), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC). The novel objective function is introduced in this study. Three input data set, including production cost, holding cost and inventory quantity are investigated. The experiment cases consist of the frequency of production cycle time in January, February and March, respectively. As a results, four algorithms are available to estimate not only the proper production plan of cashew nuts but also an ability in reducing the inventory and the holding costs. In summary, the ALO algorithm provides better predictive skill than others for the cashew nuts production plan with the lowest RMSE value of 0.0913.

Novel 2-D histogram based Soft thresholding for Brain Tumor Detection and Image Compression

The objective of image compression is to extract meaningful clusters in a given image. Significant groups are possible with absolute threshold values. 1-D histogram-based multilevel thresholding is computationally complex and reconstructed image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. So, 2-D histogram-based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel hybrid Genetic Algorithm, Particle Swarm Optimization and Symbiotic Organisms Search (hGAPSO-SOS), and the obtained results are compared with state of the art optimization techniques. Recent study reveals that PSNR fails in measuring the visual quality because of mismatch with the objective mean opinion scores (MOS). So, we incorporate a weighted PSNR (WPSNR) and visual PSNR (VPSNR). Experimental results examined on Magnetic Resonance images of brain, and results with 2-D histogram reveal that hGAPSO-SOS method can be efficiently and accurately used in multilevel thresholding problem.

A Quasioppositional-Chaotic Symbiotic Organisms Search Algorithm for Distribution Network Reconfiguration with Distributed Generations

This study suggests an enhanced metaheuristic method based on the Symbiotic Organisms Search (SOS) algorithm, namely, Quasioppositional Chaotic Symbiotic Organisms Search (QOCSOS). It aims to optimize the network configuration simultaneously and allocate distributed generation (DG) subject to the minimum real power loss in radial distribution networks (RDNs). The suggested method is developed by integrating the Quasiopposition-Based Learning (QOBL) as well as Chaotic Local Search (CLS) approaches into the original SOS algorithm to obtain better global search capacity. The proposed QOCSOS algorithm is tested on 33-, 69-, and 119-bus RDNs to verify its effectiveness. The findings demonstrate that the suggested QOCSOS technique outperformed the original SOS and provided higher-quality alternatives than many other methods studied. Accordingly, the proposed QOCSOS algorithm is favourable in adapting to the DG placement problems and optimal distribution network reconfiguration.

Optimization of concentrically braced steel frame structures based on SNI 1726:2019, SNI 1727:2020, SNI 1729:2020, and AISC 341-16

Damages resulted from earthquakes are a loss in the economic sector. The structure of multi-story buildings needs an earthquake-proof design with higher performance to reduce such losses. By utilizing the metaheuristic algorithm, this study aims to identify the most compatible brace configuration and profile used in a concentrically braced steel frame structures with minimal total weight and that will meet the safety requirements. This algorithm is suitable owing to the fact that it is able to find solutions to any known optimization problem either through Particle Swarm Optimization (PSO), Symbiotic Organisms Search (SOS), or Differential Evolution (DE). The performance of these algorithms will demonstrated in a form of comparison through a case study of optimizing a 5-span, 6-story steel frame structure. These systems will determine the lightest frame weight, which also correlates to a lower construction cost, without compromising the constraints of SNI 1726:2019, SNI 1727:2020, SNI 1729:2020, and AISC 341-16. Based on the results of data processing, SOS is shown to achieve the highest algorithm performance compared to PSO and DE.

A comparative study of several bio-inspired algorithms in cost optimization of cellular beams

Castellated beams are commonly used in steel construction. This study will focus on castellated beams with circular-shaped openings, which are known as cellular beams. Cost optimization of cellular beams is needed to maintain cost efficiency. The optimization considers the selection of a root beam, the diameter of holes, and the total number of holes in the beam as the variables. Four metaheuristic algorithms are used to optimize the design, namely, the particle swarm optimization (PSO), differential evolution (DE), symbiotic organisms search (SOS), and artificial bee colony (ABC). A four-meter span beam with a 50 kN point live load in the middle of the beam and a 5 kN/m uniformly-distributed dead load are taken as the case study. The results indicate that the SOS algorithm yields the best optimization results in terms of the average, consistency, and convergence behavior with a 30 out of 30 success rates.