Salp Swarm Algorithm: A Comprehensive Review

Houssein, Essam H.; Mohamed, Ibrahim E.; Wazery, Yaser M.

doi:10.1007/978-3-030-40977-7_13

Cited by 9 publications

(4 citation statements)

References 91 publications

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“…Increasing the number of iterations, the algorithm calculates the optimal value of F, representing the value of p SG_Best . The computation of the positions of the leader and follower salp is achieved by using Equations (12) and (20). 11.…”

Section: Work Flow Of the Proposed Algorithm (Las-iussot)mentioning

confidence: 99%

“…The traditional salp swarm intelligence has a drawback: it easily gets trapped in a local optimum solution. This drawback is eliminated in the proposed IUSSOT 11–15 . Figure 1 illustrates the impediments of three‐dimensional underwater acoustic sensor network (3D‐UASN).…”

Section: Introductionmentioning

confidence: 99%

“…This drawback is eliminated in the proposed IUSSOT. [11][12][13][14][15] Figure 1 illustrates the impediments of threedimensional underwater acoustic sensor network (3D-UASN). Impediments are challenges addressed in 3D-UASN 1,4,16,17 while computing the position of underwater sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

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An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

Yadav,

Mohan Khilar,

Sharma

2024

Int J Communication

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SummaryThe underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of underwater acoustic sensor network (UASN) include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, and military surveillance. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes needs to be identified in order to achieve effective communication. To accomplish the requirement, the paper presents an efficient localization algorithm to compensate the stratification effect based on an improved underwater salp swarm optimization technique (LAS‐IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in three‐dimensional underwater acoustic sensor network (3D‐UASN). After that, the hybridization of centroid‐based localization and the ray theory technique is used, and then, the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the computation of the location of unknown nodes is performed using improved underwater salp swarm optimization technique (IUSSOT) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS‐IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. The LAS‐IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D‐UASN, root mean square error (RMSE), normalized localization error (NLE), computation time, and convergence rate.

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Section: Work Flow Of the Proposed Algorithm (Las-iussot)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

Yadav,

Mohan Khilar,

Sharma

2024

Int J Communication

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“…Comparing the SSA algorithm with well-known and new algorithms such as PSO, GSA, BA, FPA, SMS, FA, and GA, the results show that the SSA algorithm outperforms the others in most of the test functions [45]. Due to this advantage of the SSA, it has been used in various fields such as feature selection, training neural networks, scheduling, control of power systems, and renewable energy systems [46][47][48]. In view of the excellent global optimization ability of the SSA, this paper tries to apply it to solve the path planning and traffic evacuation model.…”

Section: Analysis Of Related Workmentioning

confidence: 99%

Dynamic Emergency Vehicle Path Planning and Traffic Evacuation Based on Salp Swarm Algorithm

Duan

Xiong

2022

Journal of Advanced Transportation

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In view of the rescue delay due to traffic congestion in the urban road network, this paper implemented real-time traffic control with congestion index constraints in emergency vehicle dispatching and proposed a two-stage optimization model and algorithm. In the first stage, salp swarm algorithm (SSA) was combined with Dijkstra algorithm, and a novel hybrid algorithm with new updating rules was designed to get the multiple alternative paths. In the second stage, an improved salp swarm algorithm (ISSA) with a population grouping strategy was proposed to obtain the best evacuation schemes and the optimal rescue paths of emergency vehicles. Results of the illustrative examples show that, after evacuation, the average travel time of all alternative paths is reduced by 24.22%, while traffic congestion indexes of the adjacent road sections almost unchanged. The computation time of the hybrid algorithm for obtaining the set number of alternative paths is 56.62% and 50.47% shorter than that of bat algorithm (BA) and SSA. For the solution of the evacuation model, the computation time of the ISSA is 33.51%, 30.15%, and 30.60% shorter than that of particle swarm optimization (PSO), BA, and SSA, and the optimal solution of the ISSA is 25.92%, 10.06%, and 0.97% better than that of PSO, BA, and SSA. That is, we shorten the emergency response time and control the adverse impact of traffic evacuation on background traffic. The improved algorithm has excellent performance. This study provides a new idea and method for emergency rescue of traffic accidents.

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Multi-level Thresholding Image Segmentation Based on Nature-Inspired Optimization Algorithms: A Comprehensive Review

Houssein

Helmy

Oliva

et al. 2021

Studies in Computational Intelligence

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Salp Swarm Algorithm: A Comprehensive Review

Cited by 9 publications

References 91 publications

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

Dynamic Emergency Vehicle Path Planning and Traffic Evacuation Based on Salp Swarm Algorithm

Multi-level Thresholding Image Segmentation Based on Nature-Inspired Optimization Algorithms: A Comprehensive Review

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