An enhanced exploratory whale optimization algorithm for dynamic economic dispatch

In this work, the Whale Optimization Algorithm method used to solve the Environmental Economic Dispatch Problem. The performance of the used algorithm is substantiated using standard test system of three thermal generating units. The proposed algorithm produced optimum or near optimum solutions. The obtained results in this study using the Whale Optimization Algorithm are compared with the obtained results using other intelligent methods such as Particle Swarm Optimization, Simple Genetic Algorithm and Genetic Algorithm. The comparison demonstrated the obtained results in this research are close to these obtained using the above revealed approaches.

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Application of Whale Optimization Algorithm for Environmental Constrained Economic Dispatch of Fixed Head Hydro-Wind-Thermal Power System

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2261.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 5608-5616

Keyword(s):

Power System ◽

Wind Power ◽

Optimization Algorithm ◽

Optimization Technique ◽

Economic Dispatch ◽

Electric Power System ◽

Whale Optimization Algorithm ◽

Multi Objective ◽

Whale Optimization ◽

The Cost

This work applies whale optimization algorithm for emission constrained economic dispatch of hydrothermal units including wind power. As the wind power has a characteristic of cleanliness and is renewable, this is convincing to include this for better operation of electric power system keeping in view both economic and environmental aspects. Hydrothermal scheduling integrated with wind power establishes a multi-objective problem that becomes economic emission hydro-thermal-wind scheduling problem while taking into consideration the cost due to wind uncertainty. Whale optimization algorithm is proposed to solve this emission constrained economic dispatch problem with competing objectives. This algorithm is recently developed and gives the best solution among other nature inspired algorithms. The objectives minimum generations as well as emission cost, both are optimized together including different constraints. A daily scheduling of all the three types of systems - hydro, thermal and wind is considered to evaluate the competency of this optimization technique to get a solution for this multi-objective problem. The experiments are carried out on two systems for determining the effectiveness of the suggested method. Besides, results found using the whale optimization technique have been compared with the results obtained from other evolutionary methods. From the comparison, it is experimentally justified that the whale optimization works faster and the cost of generation as well as cost of emission are lower than the other approaches.

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Economic and Emission Dispatch using Whale Optimization Algorithm (WOA)

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i3.pp1297-1304 ◽

2018 ◽

Vol 8 (3) ◽

pp. 1297

Author(s):

Faseela C. K. ◽

H. Vennila

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Economic Dispatch ◽

Particle Swarm ◽

Test System ◽

Whale Optimization Algorithm ◽

Swarm Optimization ◽

Good Convergence ◽

Whale Optimization ◽

Bus System

<p>This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system.</p>

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Study of The Economic Dispatch Problem on IEEE 30-Bus System using Whale Optimization Algorithm

International Journal of Engineering Technology and Sciences ◽

10.15282/ijets.5.2016.1.2.1041 ◽

2016 ◽

Vol 3 (1) ◽

pp. 11-18 ◽

Cited By ~ 2

Author(s):

Haider J. Touma

Keyword(s):

Optimization Algorithm ◽

Economic Dispatch ◽

Test System ◽

Standard Test ◽

Whale Optimization Algorithm ◽

Swarm Optimization ◽

Whale Optimization ◽

New Strategy ◽

Economic Dispatch Problem ◽

Intelligent Methods

This work presents one of the latest meta heuristic optimization approaches named Whale Optimization Algorithm method as a new strategy to solve the Economic Dispatch problem . The execution of the utilized algorithm is verified using standard test system of IEEE 30-Bus. The proposed algorithm delivered optimum or close optimum solutions. The achieved results in this study using the above mentioned Algorithm have been compared with the obtained results using other intelligent methods such as Particle Swarm Optimization, Ant Colony optimization and Genetic Algorithm. The comparison explained the obtained results in this study are close to these obtained using the above revealed approaches.

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

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666200304131411 ◽

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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sEMG-Based Dynamic Muscle Fatigue Classification Using SVM with Improved Whale Optimization Algorithm

IEEE Internet of Things Journal ◽

10.1109/jiot.2021.3056126 ◽

2021 ◽

pp. 1-1

Author(s):

Quan Liu ◽

Yang Liu ◽

Congsheng Zhang ◽

Zhili Ruan ◽

Wei Meng ◽

...

Keyword(s):

Muscle Fatigue ◽

Optimization Algorithm ◽

Whale Optimization Algorithm ◽

Whale Optimization

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Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽

10.3390/s21082628 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2628

Author(s):

Mengxing Huang ◽

Qianhao Zhai ◽

Yinjie Chen ◽

Siling Feng ◽

Feng Shu

Keyword(s):

Optimization Algorithm ◽

Edge Computing ◽

Solution Set ◽

Whale Optimization Algorithm ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Multi Objective ◽

Whale Optimization ◽

Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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