SSMA: simplified slime mould algorithm for optimization wireless sensor network coverage problem

Wei, Yuanye; Wei, Xiuxi; Huang, Huajuan; Bi, Jian; Zhou, Yongquan; Du, Yanlian

doi:10.1080/21642583.2022.2084650

Cited by 12 publications

(6 citation statements)

References 46 publications

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“…Modified versions of SMA variants [30] refer to those that employ the methods of simplifying, removing, replacing original operators, or adding new operators, while the framework remains unchanged. We can adopt the SSMA [31] proposed by Yuanye Wei et al as an example. In the SSMA, the third equation in Equation ( 1) was removed and the original oscillation factor was replaced by cosine function to form a simple version of SMA variants.…”

Section: Modified Version Of the Smamentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

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Section: Modified Version Of the Smamentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

Self Cite

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“…D represents the distance between an individual wolf and its prey. The calculation method of A and D is shown in Equations ( 7) and (8).…”

Section: Gwomentioning

confidence: 99%

“…It can be found in the literature [5][6][7] that they all study swarm intelligence algorithms improve the performance of WSNs, from aspects such as node redundancy and coverage of homogeneous WSNs. Similar research includes the improved sticky algorithm proposed by Wei et al, simplified slime mold algorithm (SSMA) [8], the improved fruit fly optimization algorithm (change step of fruit fly optimization algorithm (CSFOA)) proposed by Song et al [9]. Some researchers aim to optimize the coverage of heterogeneous WSNs and study swarm intelligence algorithms that reduce node redundancy and improve coverage.…”

Section: Introductionmentioning

confidence: 99%

An Improved Grey Wolf Optimizer with Multi-Strategies Coverage in Wireless Sensor Networks

Ou,

Qin,

Zhou

et al. 2024

Symmetry

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For wireless sensor network (WSN) coverage problems, since the sensing range of sensor nodes is a circular area with symmetry, taking symmetry into account when deploying nodes will help simplify problem solving. In addition, in view of two specific problems of high node deployment costs and insufficient effective coverage in WSNs, this paper proposes a WSN coverage optimization method based on the improved grey wolf optimizer with multi-strategies (IGWO-MS). As far as IGWO-MS is concerned, first of all, it uses Sobol sequences to initialize the population so that the initial values of the population are evenly distributed in the search space, ensuring high ergodicity and diversity. Secondly, it introduces a search space strategy to increase the search range of the population, avoid premature convergence, and improve search accuracy. And then, it combines reverse learning and mirror mapping to expand the population richness. Finally, it adds Levy flight to increase the disturbance and improve the probability of the algorithm jumping out of the local optimum. To verify the performance of IGWO-MS in WSN coverage optimization, this paper rasterizes the coverage area of the WSN into multiple grids of the same size and symmetry with each other, thereby transforming the node coverage problem into a single-objective optimization problem. In the simulation experiment, not only was IGWO-MS selected, but four other algorithms were also selected for comparison, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), grey wolf optimization based on drunk walk (DGWO), and grey wolf optimization led by two-headed wolves (GWO-THW). The experimental results demonstrate that when the number of nodes for WSN coverage optimization is 20 and 30, the optimal coverage rate and average coverage rate using IGWO-MS are both improved compared to the other four comparison algorithms. To make this clear, in the case of 20 nodes, the optimal coverage rate of IGWO-MS is increased by 13.19%, 1.68%, 4.92%, and 3.62%, respectively, compared with PSO, GWO, DGWO, and GWO-THW; while IGWO-MS performs even better in terms of average coverage rate, which is 16.45%, 3.13%, 11.25%, and 6.19% higher than that of PSO, GWO, DGWO, and GWO-THW, respectively. Similarly, in the case of 30 nodes, compared with PSO, GWO, DGWO, and GWO-THW, the optimal coverage rate of the IGWO-MS is increased by 15.23%, 1.36%, 5.55%, and 3.66%; the average coverage rate is increased by 16.78%, 1.56%, 10.91%, and 8.55%. Therefore, it can be concluded that IGWO-MS has certain advantages in solving WSN coverage problems, which is reflected in that not only can it effectively improve the coverage quality of network nodes, but it also has good stability.

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“…These include a chimp-inspired optimization scheme [ 39 ], i.e., an intelligent optimization algorithm effectively exploited to solve different problems with reasonably accuracy through providing a good balance in the exploration and exploitation phases; a Kohonen neural network [ 40 ], i.e., an unsupervised self-organizing (SO) competitive neural network that performs automatic clustering and that updates the weights of the network through SO feature mapping with effective application to intrusion detection of the network virus; and a Mayfly algorithm [ 41 ], i.e., a swarm intelligence-based heuristic approach, applied to successfully solve different engineering optimization problems, including the asymmetric traveling salesman problem, due to the features of population diversity and enhanced local search capability. Others include a simplified slime mould algorithm [ 42 ], i.e., a modified version of the slime mould heuristic, with an introduction of enhanced adaptive oscillation for better exploration capability during the early search phase, with application to wireless sensor network optimization problems; a code pathfinder algorithm [ 43 ], i.e., a discrete complex code pathfinder heuristic for an efficient solution to the optimization problem of wind farm layout through an improved exploration capability; and a firefighting strategy based marine predators approach [ 44 ], i.e., an improved variant of marine predator heuristic through an introduction of opposition-based learning for more uniform initial population and adaptive weight factor for creating balance between exploration/exploitation capabilities to effectively handle the forest fire rescue issues. More of these intelligent computer algorithms include a chaotic grey wolf optimizer [ 45 ], i.e., a modified grey wolf optimizer by incorporating the concepts of chaotic maps and adaptive convergence factor for robust and accurate parameter estimation of control autoregressive systems; a subtraction average based optimizer [ 46 ], i.e., an optimization approach inspired by the subtraction average of searchers agents for the position updates of the particles in the search space; and an enhanced dragonfly heuristic [ 47 ], i.e., enhanced version of dragonfly algorithm with an improved mechanism of global search and a local search for the four color map problem.…”

Section: Related Workmentioning

confidence: 99%

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

et al. 2023

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The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson’s disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg–Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations.

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SSMA: simplified slime mould algorithm for optimization wireless sensor network coverage problem

Cited by 12 publications

References 46 publications

Advances in Slime Mould Algorithm: A Comprehensive Survey

Advances in Slime Mould Algorithm: A Comprehensive Survey

An Improved Grey Wolf Optimizer with Multi-Strategies Coverage in Wireless Sensor Networks

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

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