scholarly journals An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qian Wang ◽  
Yong Tian ◽  
Lili Lin ◽  
Ratnaji Vanga ◽  
Lina Ma
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Civil Aviation ◽  
Convergence Factor ◽  
Suggested Approach ◽  
Whale Optimization ◽  
Block Time ◽  
Better Than

Standard scheduled flight block time (SBT) setting is of great concern for Civil Aviation Administration of China (CAAC) and airlines in China. However, the standard scheduled flight block times are set in the form of on-site meetings in practice and current literature has not provided any efficient mathematical models to calculate the flight block times fairly among the airlines. The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. A novel nonlinear update equation of convergence factor for random iterations is used in place of the original linear one in the proposed IWOA algorithm. Experimental results show that the suggested approach is effective, and IWOA performs better than WOA in the concerned problem, whose solutions are better compared to the flight block times released by CAAC. In particular, it is interesting to find that MSE, RMSE, MAE, MAPE and Theil of the reliability in 60%–70% range are always the smallest and the average fairness of airlines is better than that of 60%–75% range. The model and solving approach presented in this article have great potential to be applied by CAAC to determine the standard SBTs strategically.

Improved whale optimization algorithm based on random hopping update and random control parameter

2021 ◽  
Vol 40 (1) ◽  
pp. 363-379
Author(s):  
Yanju Guo ◽  
Huan Shen ◽  
Lei Chen ◽  
Yu Liu ◽  
Zhilong Kang
Keyword(s):  
Optimization Algorithm ◽  
Control Parameter ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Original Algorithm ◽  
Multimodal Functions ◽  
Whale Optimization ◽  
Update Strategy ◽  
Random Control ◽  
Better Than

Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

scholarly journals Loss Minimization in Optimal Reactive Power Planning (ORPP) Based on Whale Optimization Algorithm

2018 ◽  
Vol 7 (3.15) ◽  
pp. 192
Author(s):  
Muhammad Hakimin Nasru ◽  
Ismail Musirin ◽  
Mohamad Khairuzzaman Mohamad Zamani ◽  
Siti Rafidah Abdul Rahim ◽  
Muhamad Hatta Hussain ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimal Allocation ◽  
Reactive Power ◽  
Transmission Loss ◽  
Whale Optimization Algorithm ◽  
Loss Minimization ◽  
Power Scheduling ◽  
Whale Optimization ◽  
Reactive Power Planning ◽  
Better Than

The key of RPP is the optimal allocation of reactive power considering location and size. This paper presents the loss minimization in optimal reactive power planning (ORPP) based on Whale Optimization Algorithm (WOA). The objective is to minimize transmission loss by considering several load conditions at bus 3, bus 15 and bus 21. Reactive Power Scheduling (RPS) and Transformer Tap Changer Setting (TTCS) were set as the control variables. Validation was conducted on the IEEE 30 Bus RTS. Results from the study indicate that the proposed WOA can minimize transmission loss better than Evolutionary Programming (EP). 

Research of Improved Whale Optimization Algorithm Based on Variable Convergence Factor and Forced Global Search

電腦學刊 ◽  
2021 ◽  
Vol 32 (5) ◽  
pp. 148-160
Author(s):  
Cheng Zhu Cheng Zhu ◽  
Xu-Hua Pan Cheng Zhu ◽  
Qi Chen Xu-Hua Pan ◽  
Yong Zhang Qi Chen ◽  
Xin-Yi Gao Yong Zhang
Keyword(s):  
Optimization Algorithm ◽  
Global Search ◽  
Whale Optimization Algorithm ◽  
Convergence Factor ◽  
Whale Optimization

scholarly journals Hybrid Whale Optimization Algorithm Based on Three Spiral Searching Strategies and Sine Cosine Operator with Convergence Factor

2021 ◽  
Author(s):  
Ji Zhang ◽  
Kai Yang ◽  
jiesheng wang
Keyword(s):  
Optimization Algorithm ◽  
Population Diversity ◽  
Whale Optimization Algorithm ◽  
Convergence Factor ◽  
Search Range ◽  
Hunting Behavior ◽  
Fireworks Algorithm ◽  
Cosine Operator ◽  
Whale Optimization ◽  
Sine Cosine Algorithm

Abstract Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm inspired by whale hunting behavior. Aiming at the defect that the spiral update mechanism in WOA may exceed the search range, three different spiral searching strategies are first proposed. The agents search with a more reasonable and broader route distribution so as to improve population diversity and traversal. Secondly, an improved sine cosine operator based on the convergence factor was proposed to improve the search efficiency of WOA, where sine search is used for global exploration and cosine search is used for local exploitation. The proposed convergence factor enables search agents to adaptively balance the exploration and exploitation phases with iterations. In the simulation experiment, the effectiveness of three spiral search strategies and sine cosine operator is verified. Then, the whale optimization algorithm (WOA), salp swarm algorithm (SSA), firefly algorithm (FA), moth-flame optimization (MFO) algorithm, fireworks algorithm (FWA), sine cosine algorithm (SCA) and improved WOA are selected for comparison experiments. Finally, the improved WOA is applied to two engineering problems (three-bar truss design problem and the welded beam optimization problem). The experimental results show that compared with other optimization algorithms, the improved WOA has the advantages of high search accuracy, fast convergence speed, and avoiding falling into local optimal values.

Adaptive whale optimization for intelligent multi-constraints power quality improvement under deregulated environment

2019 ◽  
Vol 17 (3) ◽  
pp. 490-514
Author(s):  
Niharika Thakur ◽  
Y.K. Awasthi ◽  
Manisha Hooda ◽  
Anwar Shahzad Siddiqui
Keyword(s):  
Power Quality ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Quality Improvements ◽  
Content Type ◽  
Facts Devices ◽  
Whale Optimization ◽  
Quality Issues ◽  
Better Than ◽  
Bus System

Purpose Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems. Design/methodology/approach This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions. Findings Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively. Originality/value This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

scholarly journals A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-25 ◽  
Author(s):  
Hardi M. Mohammed ◽  
Shahla U. Umar ◽  
Tarik A. Rashid
Keyword(s):  
Optimization Algorithm ◽  
Meta Analysis ◽  
Bat Algorithm ◽  
Whale Optimization Algorithm ◽  
Exploration And Exploitation ◽  
Whale Optimization ◽  
A New Technique ◽  
Nature Inspired Algorithms ◽  
Statistical Results ◽  
Better Than

The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta-analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey’s results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

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