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Wireless mesh networks (WMNs) have many advantages such as low-cost and increased high- speed wireless Internet connectivity; therefore, WMNs are becoming an important networking infrastructure. In our previous work, we implemented a particle swarm optimization (PSO)-based simulation system for node placement in WMNs, called WMN-PSO. Also, we imple...
Citations
... To eliminate the influence of index dimensionality, three techniques for preprocessing data, namely, min-max normalization, Z-score normalization and centralization, were selected to process the index data of the samples. To optimize the parameters of the LightGBM discriminant model, the genetic algorithm (GA) (Inage et al., 2024), particle swarm optimization (PSO) (Sabzzadeh et al., 2020), and simulated annealing (SA) (Sakamoto et al., 2019) were used as three optimization algorithms. Nine LightGBM models were constructed, and the most suitable model was chosen by the comprehensive consideration of F1score and model stability. ...
... Therefore, the statistical results validate the efficiency and practicability of the PMMCOA. The control performance optimized via the PMMCOA is compared with that of the GA [37], the PSO [38], the GWO [39], the MFO [40], and the GGWO [41]. Since the control signals v dr and v qr in Figure 3 may exceed the allowable value at some operation points, an upper limit on the values of v dr and v qr is necessary. ...
This work proposes a parallel multi-layer Monte Carlo optimization algorithm (PMMCOA) that optimizes proportional–integral parameters for a doubly fed induction generator-based wind turbine controller. The PMMCOA, an improved form of the Monte Carlo algorithm, realizes the optimization process via a parallel multi-layer structure. The PMMCOA includes rough search layers, precise search layers, and re-precise search layers. Each layer of the PMMCOA adopts a multi-region and multi-granularity approach to increase the diversity and randomness of the search samples. The PMMCOA is employed to tune the controller parameters for achieving maximum power point tracking and improving generation efficiency. The controller fitness function reflects the sum of the rotor angular velocity error and the reactive power error. Compared with the five metaheuristic algorithms, the PMMCOA has a higher global convergence and more accurate power tracking ability.
... The proposed APSO was introduced to optimize delay and results revealed the superiority of APSO when compared with K-mean algorithms and GA. Moreover, PSO was combined with various meta-heuristics such as distributed GA [25], SA [26], and HC [27]. ...
... Example of algorithms convergence using I nstance 1 Figures 25,26,27,28, and 29 report an example of the algorithms convergence using I nstance 1 , I nstance 2 , I nstance 3 , I nstance 4 , and I nstance 5 . ...
This paper proposes an enhanced version of Moth Flame Optimization (MFO) algorithm, called Enhanced Chaotic Lévy Opposition-based MFO (ECLO-MFO) for solving the mesh router nodes placement problem in wireless mesh network (WMN-MRNP). The proposed ECLO-MFO incorporates three strategies including the chaotic map concept, the Lévy flight strategy, and the Opposition-Based Learning (OBL) technique to enhance the optimization performance of MFO. Firstly, chaotic maps are used to increase the chaotic stochastic behavior of the MFO algorithm. Lévy flight distribution is adopted to increase the population diversity of MFO. Finally, OBL is introduced to improve the convergence speed of MFO and to explore the search space effectively. The effectiveness of the proposed ECLO-MFO is tested based on various scenarios under different settings, considering network connectivity and client coverage metrics. The results of simulation obtained using MATLAB 2020a demonstrate the accuracy and superiority of ECLO-MFO in determining the optimal positions of mesh routers when compared with the original MFO and ten other optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Harmony Search (HS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), Bat Algorithm (BA), Firefly optimization (FA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA).
... This is due to its simplicity, adaptability, and low memory requirement. To obtain an effective solution for the mesh routers placement problem, PSO has been combined with other meta-heuristics such as GA [13], SA [14], and HC [13]. ...
This paper proposes the application of the recently proposed hybrid particle swarm optimization (PSO) and firefly algorithm (FA), called HFPSO, for solving the mesh routers placement problem in wireless mesh networks (WMNs). HFPSO combines the local search ability of FA and the fast convergence ability of PSO algorithm. The effectiveness of HFPSO was demonstrated using many generated instances in comparison with FA and PSO algorithms taking into account the metrics of user coverage and network connectivity. The results showed that HFPSO is more effective than FA and PSO in finding optimal mesh routers locations.KeywordsMesh router nodes placementWireless mesh networksFirefly algorithmParticle swarm optimizationNetwork design
... where T is the system temperature, and α is cooling parameter. In this paper, we adopt the definition provided in the paper [39] to calculate the α value. ...
... In this paper, we adopt the definition provided in the paper [39] to calculate the α value. ...
With the growth of streaming data from many domains such as transportation, finance, weather, etc, there has been a surge in interest in time series data mining. With this growth and massive amounts of time series data, time series representation has become essential for reducing dimensionality to overcome the available memory constraints. Moreover, time series data mining processes include similarity search and learning of historical data tasks. These tasks require high computation time, which can be reduced by reducing the data dimensionality. This paper proposes a novel time series representation called Adaptive Simulated Annealing Representation (ASAR). ASAR considers the time series representation as an optimization problem with the objective of preserving the time series shape and reducing the dimensionality. ASAR looks for the instances in the raw time series that can represent the local trends and neglect the rest. The Simulated Annealing optimization algorithm is adapted in this paper to fulfill the objective mentioned above. We compare ASAR to three well-known representation approaches from the literature. The experimental results have shown that ASAR achieved the highest reduction in the dimensions. Moreover, it has been shown that using the ASAR representation, the data mining process is accelerated the most. The ASAR has also been tested in terms of preserving the shape and the information of the time series by performing One Nearest Neighbor (1-NN) classification and K-means clustering, which assures its ability to preserve them by outperforming the competing approaches in the K-means task and achieving close accuracy in the 1-NN classification task.
... They find that PSO generally outperforms GAs for their problem. Chakraborty et al. [47] consider PSO for the CA problem in mobile cellular networks, while Sakamoto et al. [48] present a PSO-based algorithm for node placement in WMNs. PSO is included in the Mixed Integer Linear Programming solution to a related problem, topology control in WMNs, by Rai et al. [49]. ...
In this work, we evaluate the application of four different metaheuristic optimisation algorithms for solving the channel assignment problem in a multi-radio multi-channel Wireless Mesh Network (WMN) using Dynamic Spectrum Access (DSA). The work advances a near optimal channel assignment (CA) in a WMN that uses DSA by applying soft computing methods. While CA in a WMN is well-studied, and CA for secondary user cognitive radio networks has also been studied in the literature, CA for our specific scenario of an infrastructure DSA-WMN is novel. This scenario poses new challenges because nodes are spread out geographically and so may have different allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) to avoid interference within the network and with external interference sources, and 2) maintain connectivity within the network; all while staying within the radio interface constraint, i.e., only assigning as many channels to a node as it has radios. We present a novel algorithm, used alongside the metaheuristic optimisation algorithms, which ensures the feasibility of solutions in the search space. Average Signal to Interference and Noise Ratio (
$SINR$
) over the network is used as the performance measure, with the goal of optimisation being to find the highest average
$SINR$
. This is a more realistic performance measure than the binary on/off conflict-based measures most common in the literature. Our energy-based method also has the unique advantage that it is protocol-agnostic, being able to avoid interference from external sources that use different protocols and standards. The algorithms that are compared in this work are Simulated Annealing (SA), the Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Differential Evolution (DE). These algorithms were evaluated through the use of simulation in Network Simulator 3. Various parameters were experimented with for each of the employed algorithms. The resultant best set of parameters was used for the comparison of the four metaheuristic algorithms. It was found that the population-based algorithms (PSO, GA, and DE) all perform satisfactorily for this problem, although DE is superior to the others. SA can give acceptable solutions, but performs poorly in comparison to the population-based algorithms. The paper also considers the computational complexity of the methods. It is found that SA and DE perform well in this regard.
... Sakamoto et al. [130] proposed a hybrid algorithm based on the combination of PSO and SA, called PSO-SA, for solving the mesh routers placement problem in WMNs. PSO-SA was evaluated considering four replacement methods, which are given as follows: constriction method (CM), random inertia weight method (RIWM), linear decreasing inertia weight method (LDIWM), and rational of decrement of Vmax method (RDVM). ...
Wireless mesh networks (WMNs) have grown substantially and instigated numerous deployments during the previous decade thanks to their simple implementation, easy network maintenance, and reliable service coverage. Despite these proprieties, the nodes placement of such networks presents many challenges for network operators. In this paper, we present a survey of optimization approaches implemented to address the WMNs nodes placement problem. These approaches are classified into four main categories: exact approaches, heuristic approaches, meta-heuristic approaches, and hybrid approaches. For each category, a critical analysis is drawn according to targeted objectives, considered constraints, type of positioned nodes (Mesh Router and Mesh Gateway), location (discrete or continuous), and environment (static or dynamic). In the end, several new key search areas for WMNs nodes placement are suggested.
... The communication radius between the anchor node and the unknown node is r=25m. First, analyze the impact of the particle population on the algorithm [7]. The number of particles varies from 30 to 120, the number of iterations is 100, and the other parameters of the algorithm remain unchanged. ...
The paper proposes a positioning method based on random particle swarm optimization algorithm. It is assumed that there are some anchor nodes in the wireless sensor network, and the distance information between adjacent nodes can be obtained. After the positioning node obtains enough distance and location information of the adjacent anchor nodes or the located nodes, the random particle swarm optimization algorithm is used Achieve positioning. Simulation shows that this method has higher performance than multilateral measurement method and positioning method based on standard particle swarm optimization algorithm.
... But BP is based on gradient descent method and has some potential problems and disadvantages which mainly focus on getting trapped into local minimal and weaknesses in the converging speed and determination of network architecture and network parameters. In recent decades, to deal with the complicated training problem of ANNs, many meta-heuristic optimization algorithms, such as simulated annealing (SA) (Sakamoto et al. 2019), genetic algorithm (GA) (Prakash et al. 2019) and particle swarm optimization (PSO) (Wang et al. 2018), have been utilized to optimize the weights and biases of ANN. ...
The prediction of chaotic time series is an important issue in nonlinear information procession. Due to the multi-modal, high-dimensional and non-differentiable or discontinuous characteristics of chaotic systems, global optimization techniques are required to avoid from falling into local optima for the prediction of chaotic time series. Phase-based optimization is recently proposed as a global search algorithm inspired by natural phenomena. In this paper, an improved phase-based optimization algorithm integrating stochastic interaction strategy and global optimal interaction strategy, termed interactive phase-based optimization (IPBO), is proposed to train feed-forward neural networks (FNNs) for chaotic time series prediction. The combination of stochastic interaction strategy and global optimal interaction strategy can balance the capability of exploration and exploitation in the global optimization process. To demonstrate the searching capability, sixteen widely used benchmark functions are firstly used to investigate its optimization performance. Then, the prediction effectiveness of FNNs trained by IPBO has been illustrated using classical chaotic time series of Lorenz, Box–Jenkins and Mackey–Glass. The training and testing performances of IPBO and other state-of-the-art optimization algorithms have been compared for predicting these time series. Conducted numerical experiments indicate that IPBO is not only competitive in functions optimization and has also a better learning ability in training FNNs among other state-of-the-art optimization algorithms.
This study focuses on the size of composition of lunar core. To deal with this issue, we considered the lunar mean density and mean moment of inertia factor in our inversion. We used the degree-2 coefficients of lunar gravity field model GL990D and the lunar physical liberation parameters to compute mean moment of inertia factor, which is treated as an observed value. We also computed the observed value of the mean density according the total mass of the Moon. Based on the interior structure with various layers, we deduced the modeled expressions for the lunar mean density and mean moment of inertia factor. Summing squares of the difference between the observed and modeled values as an inversion criterion, we estimated the multi-parameters based on the simulated annealing algorithm. By considering the lunar interior structure with three layers, the estimated size of the lunar core is around 470 km, and the density of the core gets close to 5486 kg·m⁻³. The computed size and density of the lunar core are close to other studies, validating our algorithm. We then considered the scenarios that the lunar core differentiated with a solid inner and a liquid outer core. The good-inversed outer core is close to 385 km, while the inner core approaches to 350 km. By using the good-inversed sizes as fixed parameters, it is found that the inner core is quite denser to 7879 kg×m⁻³ than the outer core, which is estimated around 4618 kg×m⁻³. Our result indicates that the outer core is composed by ferrous sulfide (FeS), while the inner core is composed by ferrous or ferro-nickel at 3.56 billion years ago when the lunar core dynamo ended.
