• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1142-1166
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• 2020

Salp Swarm Algorithm (SSA) is a new nature-inspired swarm optimization algorithm that mimics the swarming behavior of salps navigating and foraging in the oceans. SSA has been proved to enable to avoid local optima and enhance convergence speed benefiting from the adaptive nonlinear mechanism and salp chains. In this paper, visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. A novel SSA-based tracking framework is proposed and the analysis and adjustment of parameters are discussed experimentally. Besides, the qualitative analysis and quantitative analysis are performed to demonstrate the tracking effect of our proposed approach by comparing with ten classical tracking algorithms. Extensive comparative experimental results show that our algorithm has good performance in visual tracking, especially for abrupt motion tracking.

• Earthquakes and Structures
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• v.18 no.6
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• pp.719-730
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• 2020

Recently, population based optimization algorithms are developed to deal with a variety of optimization problems. In this paper, the salp swarm algorithm (SSA) is dramatically enhanced and a new algorithm is named Enhanced Salp Swarm Algorithm (ESSA) which is effectively utilized in optimization problems. To generate the ESSA, an opposition-based learning and merit function methods are added to standard SSA to enhance both exploration and exploitation abilities. To have a clear judgment about the performance of the ESSA, firstly, it is employed to solve some mathematical benchmark test functions. Next, it is exploited to deal with engineering problems such as optimally designing the benchmark buildings equipped with multiple tuned mass damper (MTMD) under earthquake excitation. By comparing the obtained results with those obtained from other algorithms, it can be concluded that the proposed new ESSA algorithm not only provides very competitive results, but also it can be successfully applied to the optimal design of the MTMD.

• Earthquakes and Structures
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• v.17 no.1
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• pp.63-73
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• 2019

This paper studies damage detection as an optimization problem. A new objective function based on changes in natural frequencies, and Natural Frequency Vector Assurance Criterion (NFVAC) was developed. Due to their easy and fast acquisition, natural frequencies were utilized to detect structural damages. Moreover, they are sensitive to stiffness reduction. The method presented here consists of two stages. Firstly, Finite Element Model (FEM) is updated. Secondly, damage severities and locations are determined. To minimize the proposed objective function, a new bio-inspired optimization algorithm called salp swarm was employed. Efficiency of the method presented here is validated by three experimental examples. The first example relates to three-story shear frame with two single damage cases in the first story. The second relates to a five-story shear frame with single and multiple damage cases in the first and third stories. The last one relates to a large-scale eight-story shear frame with minor damage case in the first and third stories. Moreover, the performance of Salp Swarm Algorithm (SSA) was compared with Particle Swarm Optimization (PSO). The results show that better accuracy is obtained using SSA than using PSO. The obtained results clearly indicate that the proposed method can be used to determine accurately and efficiently both damage location and severity in multi-story shear frames.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2225-2239
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• 2021

To assist in data collection, the use of a mobile sink has been widely suggested in the literature. Due to the limited sensor node's storage capacity, this manner to collect data induces huge latencies and drop packets. Their buffers will be overloaded and lead to network congestion. Recently, a new bio-inspired optimization algorithm appeared. Researchers were inspired by the swarming mechanism of salps and thus creating what is called the Salp Swarm Algorithm (SSA). This paper improves the sink mobility to enhance energy dissipation, throughput, and convergence speed by imitating the salp's movement. The new approach, named the Mobile Sink based on Modified Salp Swarm Algorithm (MSSA), is approved in a heterogeneous Wireless Sensor Network (WSN) data collection. The performance of the MSSA protocol is assessed using several iterations. Results demonstrate that our proposal surpass other literature algorithms in terms of lifespan and throughput.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1225-1232
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• 2023

The ¹⁸⁰W is the lightest isotope of Tungsten with small abundance ratio. It is slightly radioactive (α decay), with an extremely long half-life. Its separation is possible by non-conventional single withdrawal cascades. The ¹⁸⁰W is used in radioisotopes production and study of metals through gamma-ray spectroscopy. In this paper, single withdrawal cascade model is developed to evaluate multicomponent separation in non-conventional transient cascades, and available experimental results are used for validation. Numerical studies for separation of ¹⁸⁰W in a transient single withdrawal cascade are performed. Parameters affecting the separation and equilibrium time of cascade such as number of stages, cascade arrangements, feed location and flow rate for a fixed number of gas centrifuges (GC) are investigated. The Salp Swarm Algorithm (SSA) as a bio-inspired optimization algorithm is applied as a novel method to minimize the feed consumption to obtain desired concentration in the collection tank. Examining different cascade arrangements, it is observed in arrangements with more stages, the separation is further efficient. Based on the obtained results, with increasing feed flow rate, for fixed product concentration, the cascade equilibrium time decreases. Also, it is shown while the feed location is the farthest stage from the collection tank, the separation and cascade equilibrium time are well-organized. Finally, using SSA optimal parameters of the cascade is calculated, and optimal arrangement to produce 5 gr of ¹⁸⁰W with 90% concentration in the tank, is proposed.

• Smart Structures and Systems
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• v.26 no.2
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• pp.241-251
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• 2020

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

• Coupled systems mechanics
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• v.13 no.1
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• pp.1-19
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• 2024

The effectiveness of coupling with a bolted rim is assessed in this research using a newly designed optimization algorithm. The current study, which is provided here, evaluates 10 contemporary metaheuristic approaches for enhancing the coupling with bolted rim design problem. The algorithms used are particle swarm optimization (PSO), crow search algorithm (CSA), enhanced honeybee mating optimization (EHBMO), Harmony search algorithm (HSA), Krill heard algorithm (KHA), Pattern search algorithm (PSA), Charged system search algorithm (CSSA), Salp swarm algorithm (SSA), Big bang big crunch optimization (B-BBBCO), Gradient based Algorithm (GBA). The contribution of the paper isto optimize the coupling with bolted rim problem by comparing these 10 algorithms and to find which algorithm gives the best optimized result. These algorithm's performance is evaluated statistically and subjectively.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3291-3300
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• 2023

Recently molybdenum alloys have been introduced as accident tolerating materials for cladding of fuel rods. Molybdenum element has seven stable isotopes with different neutron absorption cross section used in various fields, including nuclear physics and radioisotope production. This study presents separation approaches for all intermediate isotopes of molybdenum element by squared-off cascades using a newly developed numerical code with Salp Swarm Algorithm (SSA) optimization algorithm. The parameters of cascade including feed flow rate, feed entry stage, cascade cut, input feed flow rate to gas centrifuges (GCs), and cut of the first stage are optimized to maximize both isotope recovery and cascade capacity. The squared off and squared cascades are studied, and the efficiencies are compared. The results obtained from the optimization showed that for the selected squared off cascade, Mo⁹⁴ in four separation steps, Mo⁹⁵ in five steps, Mo⁹⁶ in six steps, Mo⁹⁷ in seven steps, and Mo⁹⁸ in two steps are separated to the desired concentrations. The highest recovery factor is obtained 63% for Mo⁹⁴ separation and lowest recovery factor is found 45% for Mo⁹⁵.

• Smart Structures and Systems
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• v.26 no.3
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• pp.263-275
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• 2020

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

• Computers and Concrete
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• v.33 no.1
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• pp.77-90
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• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.