A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems

Ang, Koon Meng; Lim, Wei Hong; Isa, Nor Ashidi Mat; Tiang, Sew Sun; Wong, C. J.

doi:10.1016/j.eswa.2019.112882

Cited by 107 publications

(39 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kennedy and Eberhart first implemented the PSO algorithm and is still commonly used because of their common and effective approach to non-linear optimization [52], [53]. Rather than using evolutionary operators to control the individuals, as in other evolutionary computational algorithms, each individual in PSO flies in the search space with a velocity that is modified dynamically depending on the best position of the particle (pbest) and the companion's best position (gbest).…”

Section: B Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

View full text Add to dashboard Cite

Throughout recent years, the progress of telemonitoring and telediagnostics devices for evaluating and tracking Parkinson's (PD) disease has become increasingly important. The early detection of PD increases the consistency of the treatment of patients and ultimately allows it possible to achieve a rapid diagnostic decision from an experienced clinician. In this paper, a proposed fog-based ANFIS+PSOGWO model provided for Parkinson's disease prediction. The proposed model exploits the advantages of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) for adjusting the adaptive neuro-fuzzy inference system (ANFIS) parameters with the use of chaotic tent map for the initialization. The fog processing utilized for gathering and analyzing the data at the edge of the gateways and notifying the local community instantly. Compared to other optimization methods, many evaluation metrics used like the root mean square error (RMSE), the mean square error (MSE), the standard deviation (SD), and the accuracy and five standard datasets from repository of UCI machine learning that demonstrated the superiority of the model proposed against the grey wolf optimization (GWO), the particle swarm optimization (PSO), the differential evolution (DE), the genetic algorithm (GA), the ant colony optimization (ACO), and the standard ANFIS model. Moreover, the proposed ANFIS+PSOGWO applied for Parkinson's disease prediction and achieved an accuracy of 87.5%. The proposed ANFIS+PSOGWO compared in producing positive outcomes better than PSO, GWO, GA, ACO, DE, and some recent literature for Parkinson's disease prediction. The proposed model produced accuracy for the Parkinson's disease prediction has outperformed its closest competitors in all algorithms by 7.3%.

show abstract

Section: B Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

View full text Add to dashboard Cite

show abstract

“…Table 7 gives the mean and standard deviation of objective values with respect to CETDE and nine competing algorithms. Among all nine competitors, the first four are DEbased methods (i.e., DPDE [31], rank-iMDDE [36], eDE [44], and FRC-CEA [28]), the next four are non-DE-based methods (i.e., AIS [45], CMPSOWV [46], I-ABC [47] and ITLBO [48]), and the last one is a PSO-and DE-based hybrid method (i.e., DPD [49]). Since the best feasible optimal values are known, we compare the mean function values with the best known optima.…”

Section: B General Performance Of Cetde On Ieee Cec2006 Problemsmentioning

confidence: 99%

Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

Constrained optimization problems (COPs) are common in many fields, and the search algorithm and constraint handling technique play important roles in the constrained evolution algorithms. In this paper, we propose a new optimization algorithm named CETDE based on ensemble differential evolution (DE) and a two-level epsilon-constrained method. In the ensemble DE variant, some promising parameters and mutation strategies constitute the candidate pool, and each element in the pool coexists throughout the search process and competes to generate new solutions. The two-level epsilon method is proposed by incorporating a generation and a population comparison level to retain more promising solutions without degrading the solution quality. Moreover, a diversity promotion scheme is developed to improve the population distribution when the search becomes trapped in a small region. The superior performance of CETDE is validated by comparison with some state-of-the-art COEAs over two sets of artificial benchmarks and five real-world problems. The competitive results show that CETDE is an effective method for solving COPs.

show abstract

“…Due to its simple updating formulas and excellent searching ability, PSO has been widely applied in clustering analysis [17] and other optimization fields, e.g., single-and multi-objective problems [18], artificial neural network improving problem [19], assignment problem [20], and optimum battery energy storage problem [21]. However, the canonical PSO still must be improved with respect to parameter setting [22]- [24], updating strategy [25]- [28], and convergence theory [29]- [31], among others. Particularly, Wen et al [32] introduced Gauss distribution function into PSO, and the obtained Gaussian-PSO had excellent optimization performance, which was also verified by Higashi and Iba [33] later.…”

Section: Introductionmentioning

confidence: 99%

A Novel k-MPSO Clustering Algorithm for the Construction of Typical Driving Cycles

Wei

Zhong

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The practical driving cycle is of great significance in studying the control strategy of vehicles, and effective clustering of micro-trips is the key to obtaining the typical driving cycle. A novel and efficient method for constructing typical driving cycles is presented in this paper. First, by combining the preying behavior and random behavior of the artificial fish swarm algorithm (AFSA) with particle swarm optimization (PSO), a modified particle swarm optimization (MPSO) is proposed. By comparing the means and standard deviations of the optima, MPSO is verified as much more accurate and stable than PSO, AFSA, select particle swarm optimization (SPSO) and cross particle swarm optimization (CPSO) in the optimization calculation of four typical multi-modal benchmark functions. Second, by applying MPSO to optimize the k-means algorithm, the k-MPSO clustering algorithm is obtained. In the case of clustering the Iris standard data set, the average error rates of the k-means algorithm and k-MPSO clustering algorithm are 11.6% and 7.8%, respectively, which means that the k-MPSO clustering algorithm has a stronger searching ability. Finally, with the ECAN Tools software, real-world driving data that include thousands of micro-trips in Jinan are collected, and 19 representative characteristic parameters are selected to fully describe the driving conditions. After principal component analysis (PCA), the k-MPSO clustering algorithm method is applied to cluster the micro-trips into three classes and construct the typical driving cycle in Jinan. INDEX TERMS Artificial fish swarm algorithm, driving cycle, k-MPSO clustering algorithm, particle swarm optimization.

show abstract

A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems

Cited by 107 publications

References 65 publications

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

A Novel k-MPSO Clustering Algorithm for the Construction of Typical Driving Cycles

Contact Info

Product

Resources

About