An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization

2018 ◽  
Vol 68 ◽  
pp. 63-80 ◽  
Author(s):  
Wen Long ◽  
Jianjun Jiao ◽  
Ximing Liang ◽  
Mingzhu Tang
Keyword(s):  
Numerical Optimization ◽  
High Dimensional ◽  
Grey Wolf Optimizer ◽  
Grey Wolf

An efficient and robust grey wolf optimizer algorithm for large-scale numerical optimization

2019 ◽  
Vol 24 (2) ◽  
pp. 997-1026 ◽  
Author(s):  
Wen Long ◽  
Shaohong Cai ◽  
Jianjun Jiao ◽  
Mingzhu Tang
Keyword(s):  
Numerical Optimization ◽  
Large Scale ◽  
Grey Wolf Optimizer ◽  
Grey Wolf

scholarly journals Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1233
Author(s):  
Yule Wang ◽  
Wanliang Wang
Keyword(s):  
Differential Evolution ◽  
Knapsack Problem ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Knapsack Problems ◽  
High Dimensional ◽  
Adaptive Mutation ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Combinatorial Optimization Problems

The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantum-inspired differential evolution algorithm with grey wolf optimizer (QDGWO) to enhance the diversity and convergence performance and improve the performance in high-dimensional cases for 0-1 knapsack problems. The proposed algorithm adopts quantum computing principles such as quantum superposition states and quantum gates. It also uses adaptive mutation operations of differential evolution, crossover operations of differential evolution, and quantum observation to generate new solutions as trial individuals. Selection operations are used to determine the better solutions between the stored individuals and the trial individuals created by mutation and crossover operations. In the event that the trial individuals are worse than the current individuals, the adaptive grey wolf optimizer and quantum rotation gate are used to preserve the diversity of the population as well as speed up the search for the global optimal solution. The experimental results for 0-1 knapsack problems confirm the advantages of QDGWO with the effectiveness and global search capability for knapsack problems, especially for high-dimensional situations.

scholarly journals Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1581
Author(s):  
Wenqiang Zhu ◽  
Jiang Guo ◽  
Guo Zhao ◽  
Bing Zeng
Keyword(s):  
Power Supply ◽  
Tidal Current ◽  
System Optimization ◽  
Energy System ◽  
Energy Sources ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Multi Objective

The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability and stability, particularly compared with the intermittent wind and solar energy. In this paper, an island hybrid energy microgrid composed of photovoltaic, wind, tidal current, battery and diesel is constructed according to the actual energy sources. A sizing optimization method based on improved multi-objective grey wolf optimizer (IMOGWO) is presented to optimize the hybrid energy system. The proposed method is applied to determine the optimal system size, which is a multi-objective problem including the minimization of annualized cost of system (CACS) and deficiency of power supply probability (DPSP). MATLAB software is utilized to program and simulate the hybrid energy system. Optimization results confirm that IMOGWO is feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. Furthermore, comparison of hybrid systems with and without tidal current turbines is undertaken to confirm that the utilization of tidal current turbines can contribute to enhancing system reliability and reducing system investment, especially in areas with abundant tidal energy sources.

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