A whale optimization algorithm (WOA) approach for clustering

Nasiri, Jhila; Khiyabani, Farzin Modarres

doi:10.1080/25742558.2018.1483565

Cited by 200 publications

(93 citation statements)

References 29 publications

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“…WOA is considered to be a global optimizer as compared to PSO and GA, which do not guarantee global optima and are considered as a local optimizer. Also, PSO slows down near global optima because the position of particles is mainly dependent on its personal best (Pbest ) and global best (Gbest), thus, increasing its chances of jamming at some local optima instead of the global optima [44]. From Figure 11, we can see that at the base case reactive loading, WOA converged at 0.9995 × 10 7 ($) as compared to PSO and GA, which converged at 1.0714 × 10 7 ($) and 1.012 × 10 7 ($), respectively.…”

Section: Percentagementioning

confidence: 99%

Optimal Placement, Sizing and Coordination of FACTS Devices in Transmission Network Using Whale Optimization Algorithm

et al. 2020

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Flexible AC Transmission Systems (FACTS) play an important role in minimizing power losses and voltage deviations while increasing the real power transfer capacity of transmission lines. The extent to which these devices can provide benefits to the transmission network depend on their optimal location and sizing. However, finding appropriate locations and sizes of these devices in an electrical network is difficult since it is a nonlinear problem. This paper proposes a technique for the optimal placement and sizing of FACTS, namely the Thyristor-Controlled Series Compensators (TCSCs), Shunt VARs Compensators (SVCs), and Unified Power Flows Controllers (UPFCs). To find the optimal locations of these devices in a network, weak buses and lines are determined by constructing PV curves of load buses, and through the line stability index. Then, the whale optimization algorithm (WOA) is employed not only to find an ideal ratings for these devices but also the optimal coordination of SVC, TCSC, and UPFC with the reactive power sources already present in the network (tap settings of transformers and reactive power from generators). The objective here is the minimization of the operating cost of the system that consists of active power losses and FACTS devices cost. The proposed method is applied to the IEEE 14 and 30 bus systems. The presented technique is also compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The findings showed that total system operating costs and transmission line losses were considerably reduced by WOA as compared to existing metaheuristic optimization techniques.

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Section: Percentagementioning

confidence: 99%

Optimal Placement, Sizing and Coordination of FACTS Devices in Transmission Network Using Whale Optimization Algorithm

et al. 2020

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“…This mechanism is performed by a software agent, referred to as the "Incoming Data Handler" (IDH), whose pseudocode is reported in Algorithm 4 to further clarify this process and allow for its implementation. if merged is false then 6: if m p is density reachable to mc then 7: if new radius ≤ mc then 8: Merge m p with mc 9: else 10: Add m p to p-microclusters 11: end if 12: merged = true 13: end if 14: end if 15: end for 16: if merged is false then 17: for each mc in o-microclusters do 18: if merged is false then 19: if m p is density reachable to mc then 20: if new radius ≤ mc then 21: Merge m p with mc 22: merged = true 23: end if 24: end if 25: end if 26: end for 27: end if 28: if merged is false then 29: Add m p to o-microclusters 30: end if 31: end 32: return…”

Section: Handling Incoming Data Pointsmentioning

confidence: 99%

“…Amongst the most commonly used optimisation paradigms of this kind, it is worth mentioning the established Differential Evolution (DE) framework [8][9][10], as well as more recent nature inspired algorithms from the Swarm Intelligence (SI) field, such as the Whale Optimisation Algorithm (WOA) [11] and the Bat-inspired algorithm in [12], here referred to as BAT. Although the literature is replete with examples of data clustering strategies based on DE, WOA and BAT for the static domain, as, e.g., those presented in [13][14][15][16], little has been done for the dynamic environment due to the difficulties in handling data streams. The current state of dynamic clustering is therefore unsatisfactory as it mainly relies on algorithms based on techniques such as density microclustering and density grid based clustering, which require the tuning of several parameters to work effectively [17].…”

Section: Introductionmentioning

confidence: 99%

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

et al. 2019

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This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses "microclusters" and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream's robustness to noise and resiliency to parameter changes.

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“…Three major stages of this algorithm are (a) shrinking encircling hunt, (b) exploitation (i.e., bubble-net attacking), and (c) exploration (i.e., searching for the prey). More information about the WOA can be found in previous studies [37][38][39][40]. The LCA was proposed by Kashan [41], based on sporting competitions in sports leagues.…”

Section: Hybrid Metaheuristic Algorithmsmentioning

confidence: 99%

Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils

2019

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This study outlines the applicability of four metaheuristic algorithms, namely, whale optimization algorithm (WOA), league champion optimization (LCA), moth–flame optimization (MFO), and ant colony optimization (ACO), for performance improvement of an artificial neural network (ANN) in analyzing the bearing capacity of footings settled on two-layered soils. To this end, the models estimate the stability/failure of the system by taking into consideration soil key factors. The complexity of each network is optimized through a sensitivity analysis process. The performance of the ensembles is compared with a typical ANN to evaluate the efficiency of the applied optimizers. It was shown that the incorporation of the WOA, LCA, MFO, and ACO algorithms resulted in 14.49%, 13.41%, 18.30%, and 35.75% reductions in the prediction error of the ANN, respectively. Moreover, a ranking system is developed to compare the efficiency of the used models. The results revealed that the ACO–ANN performs most accurately, followed by the MFO–ANN, WOA–ANN, and LCA–ANN. Lastly, the outcomes demonstrated that the ACO–ANN can be a promising alternative to traditional methods used for analyzing the bearing capacity of two-layered soils.

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A whale optimization algorithm (WOA) approach for clustering

Cited by 200 publications

References 29 publications

Optimal Placement, Sizing and Coordination of FACTS Devices in Transmission Network Using Whale Optimization Algorithm

Optimal Placement, Sizing and Coordination of FACTS Devices in Transmission Network Using Whale Optimization Algorithm

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils

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