Ant Lion Optimization Technique for Minimization of Voltage Deviation Through Optimal Placement of Static VAR Compensator

2020 ◽  
pp. 247-256
Author(s):  
Stita Pragnya Dash ◽  
K. R. Subhashini ◽  
J. K. Satapathy
Keyword(s):  
Optimization Technique ◽  
Optimal Placement ◽  
Static Var Compensator ◽  
Ant Lion Optimization ◽  
Voltage Deviation ◽  
Ant Lion

scholarly journals Optimal Placement and Capacity of UPFC using JMFALO Technique to Upgrade Power System Dynamic Stability

2019 ◽  
Vol 9 (1) ◽  
pp. 737-746
Keyword(s):  
Power System ◽  
Dynamic Stability ◽  
Power Loss ◽  
Reactive Power ◽  
Optimization Technique ◽  
Hybrid Optimization ◽  
Optimal Placement ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Installation Cost

This paper proposes an optimal placement and capacitance of UPFC for enhancing the dynamic stability of a power system using a new hybrid optimization technique. The hybrid optimization technique is the joined execution of both the moth flame optimization (MFO) and ant lion optimization (ALO) and hence it is named as Joined moth flame ant lion optimization (JMFALO) technique. Here, the MFO technique optimizes the maximum power loss line as the suitable location of the UPFC by considering the variation available in the bus system power flow parameters like voltage, angle, real power and reactive power. Dependent on the ALO technique the influenced location parameters and dynamic stability constraints are restored into secure limits utilizing the optimum capacity of the UPFC with least voltage deviation, loss of power, and reduced installation cost. Subsequently, to restore the dynamic stability constraints at a secure limit the optimized UPFC capacity is utilized. The dynamic stability constraint of the system is power balance constraint, active and reactive power loss, and UPFC installation cost and bus voltage constraints. The proposed technique is implemented in MATLAB/Simulink working platform. The performance of the proposed technique is assessed by utilizing the comparison analysis with the existing techniques.

scholarly journals Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

2021 ◽  
Vol 13 (6) ◽  
pp. 3198
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Residential Buildings ◽  
Optimization Technique ◽  
Hybrid Algorithms ◽  
Evolutionary Strategy ◽  
Multi Layer Perceptron ◽  
Invasive Weed ◽  
Dragonfly Algorithm ◽  
Ant Lion Optimization ◽  
Heating Load ◽  
Ant Lion

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

scholarly journals Synthesizing multi-layer perceptron network with ant lion, biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings

2021 ◽  
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Residential Buildings ◽  
Optimization Technique ◽  
Hybrid Algorithms ◽  
Evolutionary Strategy ◽  
Multi Layer Perceptron ◽  
Invasive Weed ◽  
Dragonfly Algorithm ◽  
Ant Lion Optimization ◽  
Heating Load ◽  
Ant Lion

The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

A new metaheuristic unscented Kalman filter for state vector estimation of the induction motor based on Ant Lion optimizer

2018 ◽  
Vol 37 (3) ◽  
pp. 1054-1068 ◽  
Author(s):  
Marouane Rayyam ◽  
Malika Zazi ◽  
Youssef Barradi
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Content Type ◽  
Ant Lion Optimization ◽  
Ant Lion

PurposeTo improve sensorless control of induction motor using Kalman filtering family, this paper aims to introduce a new metaheuristic optimizer algorithm for online rotor speed and flux estimation.Design/methodology/approachThe main problem with unscented Kalman filter (UKF) observer is its sensibility to the initial values of Q and R. To solve the optimal solution of these matrices, a novel alternative called ant lion optimization (ALO)-UKF is introduced. It is based on the combination of the classical UKF observer and a nature-inspired metaheuristic algorithm, ALO.FindingsSynthesized ALO-UKF has given good results over the famous extended Kalman filter and the classical UKF observer in terms of accuracy and dynamic performance. A comparison between ALO and particle swarm optimization (PSO) was established. Simulations illustrate that ALO recovers rapidly and accurately while PSO has a slower convergence.Originality/valueUsing the proposed approach, tuning the design matrices Q and R in Kalman filtering becomes an easy task with a high degree of accuracy and the constraints of time cost are surmounted. Also, ALO-UKF is an efficient tool to improve estimation performance of states and parameters’ uncertainties of the induction motor. Related optimization technique can be extended to faults monitoring by online identification of their corresponding signatures.

scholarly journals Synthesizing Multi-Layer Perceptron Network with Ant Lion, Biogeography-Based, Dragonfly Algorithm, Evolutionary Strategy, Invasive Weed, and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

2021 ◽  
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Residential Buildings ◽  
Optimization Technique ◽  
Hybrid Algorithms ◽  
Evolutionary Strategy ◽  
Multi Layer Perceptron ◽  
Invasive Weed ◽  
Dragonfly Algorithm ◽  
Ant Lion Optimization ◽  
Heating Load ◽  
Ant Lion

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

scholarly journals A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique

2018 ◽  
Vol 9 (4) ◽  
pp. 2101-2109 ◽  
Author(s):  
Mohammad Jafar Hadidian-Moghaddam ◽  
Saber Arabi-Nowdeh ◽  
Mehdi Bigdeli ◽  
Davood Azizian
Keyword(s):  
Distributed Generation ◽  
Optimization Technique ◽  
Optimal Sizing ◽  
Multi Objective ◽  
Ant Lion Optimization ◽  
Ant Lion

scholarly journals Energy efficient WSN using hybrid modification PEGASIS with ant lion optimization

2021 ◽  
Vol 23 (1) ◽  
pp. 273
Author(s):  
Ahmed Abdul Azeez Asmael ◽  
Basman Al-Nedawe
Keyword(s):  
Optimization Technique ◽  
Sensor Nodes ◽  
Measure Data ◽  
Medium Access ◽  
Wireless Sensor Nodes ◽  
Original Algorithm ◽  
Ant Lion Optimization ◽  
Physical Environments ◽  
Gain Energy ◽  
Ant Lion

<p>Wireless sensor nodes consist of tiny electronic devices that can sense, transmit, and measure data from physical environments such as the field of minter surveillance. These sensor nodes significantly depend on batteries to gain energy which is used to operations associated with communication and computation. Generally, designing communication protocols is feasible to achieve effective usage of these energy resources of the sensor node. Both reported medium access control and routing can achieve energy-saving that supporting real time functionality. This paper emphasizes the use of hybrid modified PEGASIS-Ant lion optimization. Several steps are entailed in this research. First is random distribution of node followed by clustering the map as a circular region. Then, the nodes are connected to the closest node in that region. In consequence, PEGASIS-Ant lion optimization is applied to enhance the connection of the nodes and accomplish the maximum life batter of the sensor. At last, the experiments performed in this work demonstrate that the proposed optimization technique operates well in terms of network latency, power duration and energy’s consumption. Furthermore, the life span of the nodes has improved greatly by 87% over the original algorithm that accomplished a rate of life nodes of 60%.</p>

scholarly journals A hybrid ant lion optimization chicken swarm optimization algorithm for charger placement problem

2021 ◽  
Author(s):  
Sanchari Deb ◽  
Xiao-Zhi Gao
Keyword(s):  
Power Distribution ◽  
Complex Problem ◽  
Optimal Placement ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Local Optima ◽  
Charging Infrastructure ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Chicken Swarm Optimization

AbstractTransportation electrification is known to be a viable alternative to deal with the alarming issues of global warming, air pollution, and energy crisis. Public acceptance of Electric Vehicles (EVs) requires the availability of charging infrastructure. However, the optimal placement of chargers is indeed a complex problem with multiple design variables, objective functions, and constraints. Chargers must be placed with the EV drivers’ convenience and security of the power distribution network being taken into account. The solutions to such an emerging optimization problem are mostly based on metaheuristics. This work proposes a novel metaheuristic considering the hybridization of Chicken Swarm Optimization (CSO) with Ant Lion Optimization (ALO) for effectively and efficiently coping with the charger placement problem. The amalgamation of CSO with ALO can enhance the performance of ALO, thereby preventing it from getting stuck in the local optima. Our hybrid algorithm has the strengths from both CSO and ALO, which is tested on the standard benchmark functions as well as the above charger placement problem. Simulation results demonstrate that it performs moderately better than the counterpart methods.

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