An Improved Dual Grey Wolf Optimization Algorithm for Unit Commitment Problem

Liu, Jian; Liu, Sanming

doi:10.1007/978-981-10-6373-2_16

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…During each iteration update, the position of the grey wolf is estimated by the best three levels of positions. X (t+1) is the updated position of the next generation of wolves, and each candidate solution will update the distance between them and the prey [57]. In summary, the grey wolf optimizer begins to randomly initialize the population, and then updates the position of the candidate solution each according to the three wolves with the best fitness, namely, α wolf, β wolf, and δ wolf.…”

Section: Basic Principle Of Gwomentioning

confidence: 99%

An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems

et al. 2020

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Grey wolf optimizer (GWO) is a new meta-heuristic algorithm. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three main stages of hunting include: encircling, tracking and attacking. It is easy to fall into local optimum when used to optimize high-dimensional data, and there is imbalance between exploration and exploitation. An improved grey wolf optimizer based on tracking mode and seeking mode is proposed to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The algorithm is verified by simulation experiments in three parts. Firstly, the proposed grey wolf optimizer based on tracking mode (TGWO), the improved grey wolf optimizer based on seeking mode (SGWO), the improved grey wolf optimizer based on tracking and seeking mode (TSGWO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA) and Moth-flame Optimization (MFO) are adopted to optimize 21 typical benchmark functions respectively, and the obtained statistical simulation results are compared; Secondly, the improved algorithm proposed in this paper is compared with Binary Grey Wolf Optimizer (BGWO), Hybrid PSOGWO Optimization (PSOGWO) and GWO Algorithm Integrated with Cuckoo Search (GWOCS); Finally, it is applied to the lightest design engineering problem of pressure vessels. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved grey wolf optimizer based on tracking mode and seeking mode can better solve function optimization and classical engineering problems with constraints. It was found the improved grey wolf optimizer based on tracking mode has the high precision and the characteristics of balanced exploration and exploitation. INDEX TERMS Grey wolf optimizer, tracking mode, seeking mode, function optimization.

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Section: Basic Principle Of Gwomentioning

confidence: 99%

An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems

et al. 2020

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“…Recent trends reveal that the intelligent computational methods are usually adopted for multi-objective discrete problems such as clustering in WSNs [ 31 , 32 , 33 ]. The dual gray wolf optimization (GWO) algorithm has been refined, and binary and dogmatic components have been proposed in [ 34 ]. However, it has been observed that out of 29 objectives, GWO is not used for the load-balanced clustering of WSNs.…”

Section: Introductionmentioning

confidence: 99%

GWLBC: Gray Wolf Optimization Based Load Balanced Clustering for Sustainable WSNs in Smart City Environment

Singh

Nikolovski

Chakrabarti

2022

Sensors

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In a smart city environment, with increased demand for energy efficiency, information exchange and communication through wireless sensor networks (WSNs) plays an important role. In WSNs, the sensors are usually operating in clusters, and they are allowed to restructure for effective communication over a large area and for a long time. In this scenario, load-balanced clustering is the cost-effective means of improving the system performance. Although clustering is a discrete problem, the computational intelligence techniques are more suitable for load balancing and minimizing energy consumption with different operating constraints. The literature reveals that the swarm intelligence-inspired computational approaches give excellent results among population-based meta-heuristic approaches because of their more remarkable exploration ability. Conversely, in this work, load-balanced clustering for sustainable WSNs is presented using improved gray wolf optimization (IGWO). In a smart city environment, the significant parameters of energy-efficient load-balanced clustering involve the network lifetime, dead cluster heads, dead gateways, dead sensor nodes, and energy consumption while ensuring information exchange and communication among the sensors and cluster heads. Therefore, based on the above parameters, the proposed IGWO is compared with the existing GWO and several other techniques. Moreover, the convergence characteristics of the proposed algorithm are demonstrated for an extensive network in a smart city environment, which consists of 500 sensors and 50 cluster heads deployed in an area of 500 × 500 m2, and it was found to be significantly improved.

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Application of Modified Social Spider Algorithm on Unit Commitment Solution Considering the Uncertainty of Wind Power in Restructured Electricity Market

Shayanfar

Shayeghi

Bagherzadeh

2021

Transactions on Computational Science and Computational Intelligence

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An Improved Dual Grey Wolf Optimization Algorithm for Unit Commitment Problem

Cited by 3 publications

References 7 publications

An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems

An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems

GWLBC: Gray Wolf Optimization Based Load Balanced Clustering for Sustainable WSNs in Smart City Environment

Application of Modified Social Spider Algorithm on Unit Commitment Solution Considering the Uncertainty of Wind Power in Restructured Electricity Market

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