Binary Grey Wolf Optimizer for large scale unit commitment problem

Panwar, Lokesh Kumar; K, Srikanth Reddy; Verma, Abhishek; Panigrahi, B. K.; Kumar, Rajesh

doi:10.1016/j.swevo.2017.08.002

Cited by 142 publications

(84 citation statements)

References 43 publications

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“…Binary GWO 2018, [44] There are different largescale problems. Unite commitment problem was one of those problems that could be solved by using BGWO.…”

Section: Chaotic Gwomentioning

confidence: 99%

A Novel Hybrid GWO with WOA for Global Numerical Optimization and Solving Pressure Vessel Design

Mohammed¹,

Rashid²

2020

Preprint

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Note: This paper has been accepted by the journal of neural computing and applications. A recent metaheuristic algorithm, such as Whale Optimization Algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey Wolf Optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and Fitness Dependent Optimizer (FDO).

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“…Binary GWO 2018, [44] There are different largescale problems. Unite commitment problem was one of those problems that could be solved by using BGWO.…”

Section: Chaotic Gwomentioning

confidence: 99%

A Novel Hybrid GWO with WOA for Global Numerical Optimization and Solving Pressure Vessel Design

Mohammed¹,

Rashid²

2020

Preprint

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“…S-shaped and V-shaped transfer functions were successfully applied to binarize the butterfly optimization algorithm [39], the grasshopper optimization algorithm [40], dragonfly algorithm [41], the gray wolf algorithm [42], the salp swarm algorithm [43], and the brainstorm algorithm [44]; they were also used to construct hybrid binary algorithms [45].…”

Section: Transfer Functionsmentioning

confidence: 99%

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

et al. 2019

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.

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“…A schematic diagram of this problem is shown in Figure 7. e objective of this problem is to minimize the volume (X) by adjusting the cross-sectional area (x, y) as per (16) subject to the constraints [17][18][19].…”

Section: Ree-bar Truss Design Problemmentioning

confidence: 99%

“…e applications of swarm algorithms are very well reported in the literature and in many design problems, namely, Automatic Generation Control [17], Unit Commitment [18,19], Feature Selection [20], and Ambient Air Quality Classification [21].…”

Section: Introductionmentioning

confidence: 99%

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

Saxena

Shekhawat

Kumar

2018

Modelling and Simulation in Engineering

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In recent years, metaheuristic algorithms have revolutionized the world with their better problem solving capacity. Any metaheuristic algorithm has two phases: exploration and exploitation. e ability of the algorithm to solve a difficult optimization problem depends upon the efficacy of these two phases. ese two phases are tied with a bridging mechanism, which plays an important role. is paper presents an application of chaotic maps to improve the bridging mechanism of Grasshopper Optimisation Algorithm (GOA) by embedding 10 different maps. is experiment evolves 10 different chaotic variants of GOA, and they are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). e performance of these variants is tested over ten shifted and biased unimodal and multimodal benchmark functions. Further, the applications of these variants have been evaluated on three-bar truss design problem and frequency-modulated sound synthesis parameter estimation problem. Results reveal that the chaotic mechanism enhances the performance of GOA. Further, the results of the Wilcoxon rank sum test also establish the efficacy of the proposed variants.

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Binary Grey Wolf Optimizer for large scale unit commitment problem

Cited by 142 publications

References 43 publications

A Novel Hybrid GWO with WOA for Global Numerical Optimization and Solving Pressure Vessel Design

A Novel Hybrid GWO with WOA for Global Numerical Optimization and Solving Pressure Vessel Design

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

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