A Novel Grey Wolf Optimizer with Random Walk Strategies for Constrained Engineering Design

2018 ◽  
Author(s):  
Tong Han ◽  
Xiaofei Wang ◽  
Yajun Liang ◽  
Zhenglei Wei ◽  
Yawei Cai
Keyword(s):  
Random Walk ◽  
Engineering Design ◽  
Grey Wolf Optimizer ◽  
Grey Wolf

Sine cosine grey wolf optimizer to solve engineering design problems

2020 ◽  
Author(s):  
Shubham Gupta ◽  
Kusum Deep ◽  
Hossein Moayedi ◽  
Loke Kok Foong ◽  
Assif Assad
Keyword(s):  
Engineering Design ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Design Problems ◽  
Engineering Design Problems

Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)

2020 ◽  
pp. 101-122
Author(s):  
Anjana Gosain ◽  
Kavita Sachdeva
Keyword(s):  
Random Walk ◽  
Materialized Views ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
View Selection ◽  
Materialized View ◽  
Bee Colony ◽  
Search Optimization ◽  
Materialized View Selection ◽  
Selection Of

Optimal selection of materialized views is crucial for enhancing the performance and efficiency of data warehouse to render decisions effectively. Numerous evolutionary optimization algorithms like particle swarm optimization (PSO), genetic algorithm (GA), bee colony optimization (BCO), backtracking search optimization algorithm (BSA), etc. have been used by researchers for the selection of views optimally. Various frameworks like multiple view processing plan (MVPP), lattice, and AND-OR view graphs have been used for representing the problem space of MVS problem. In this chapter, the authors have implemented random walk grey wolf optimizer (RWGWO) algorithm for materialized view selection (i.e., RWGWOMVS) on lattice framework to find an optimal set of views within the space constraint. RWGWOMVS gives superior results in terms of minimum total query processing cost when compared with GA, BSA, and PSO algorithm. The proposed method scales well on increasing the lattice dimensions and on increasing the number of queries triggered by users.

A highly efficient approach for bi-level programming problems based on dominance determination

2021 ◽  
pp. 1-42
Author(s):  
Guan Wang ◽  
Qiang Zou ◽  
Chuke Zhao ◽  
Yusheng Liu ◽  
xiaoping YE
Keyword(s):  
System Design ◽  
Engineering Design ◽  
Nonlinear Problems ◽  
Electronic System ◽  
The Other ◽  
New Method ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Computational Structure ◽  
Determination Process

Abstract Bi-level programming, where one objective is nested within the other, is widely used in engineering design, e.g., structural optimization and electronic system design. One major issue of current solvers for these bi-level problems is their low computational efficiency, especially for complex nonlinear problems. To solve this issue, a new method based on bi-level grey wolf optimizer is proposed in this paper. The basic idea is to drop the time-consuming nested computational structure commonly used by existing methods and instead use a simultaneous computational structure built on top of a dominance determination process for the grey wolf optimizer. The effectiveness of this new method has been validated with ten benchmark functions and two engineering design examples, as well as comparisons with three important existing methods in the bi-level programming domain.

A novel Random Walk Grey Wolf Optimizer

2019 ◽  
Vol 44 ◽  
pp. 101-112 ◽  
Author(s):  
Shubham Gupta ◽  
Kusum Deep
Keyword(s):  
Random Walk ◽  
Grey Wolf Optimizer ◽  
Grey Wolf

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.

Optimizing cetane improver concentration in biodiesel-diesel blend via grey wolf optimizer algorithm

Fuel ◽  
2020 ◽  
Vol 273 ◽  
pp. 117784 ◽  
Author(s):  
Erol Ileri ◽  
Aslan Deniz Karaoglan ◽  
Sener Akpinar
Keyword(s):  
Grey Wolf Optimizer ◽  
Grey Wolf
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