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.

scholarly journals Economic Load Dispatch

2021 ◽  
Vol 9 (VI) ◽  
pp. 1646-1651
Author(s):  
Prof. Kanika Lamba
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

ELD or Economic load dispatch is an online process of allocating generating among the available generating units to minimize the total generating cost and satisfy the equality and inequality constraint. ELD means the real and reactive power of the generator vary within the certain limits and fulfils theload demand with less fuel cost. There are some traditional methods for = 1; 2; :::;N) isgiven as Vi=[Vi;1; Vi;2; :::; Vi;D]. The index ivaries from solving ELD include lambda irritation method, Newton-Raphson method, Gradient method, etc. All these traditional algorithms need the incremental fuel cost curves of the generators to be increasing monotonically or piece-wise linear. But in practice the input-output characteristics of a generator are highly non-linear leading to a challenging non-convex optimization problem. Methods like artificial intelligence, DP (dynamic programming), GA (genetic algorithms), and PSO (particle swarm optimization), ALO ( ant-lion optimization), solve non convex optimization problems in an efficient manner and obtain a fast and near global and optimum solution. In this project ELD problem has been solved using Lambda-Iterative technique, ALO (ant-lion Optimization) and PSO (Particle Swarm Optimization) and the results have been compared. All the analyses have been made in MATLAB environment

scholarly journals An improved form of the ant lion optimization algorithm for image clustering problems

2019 ◽  
pp. 1445-1460 ◽  
Author(s):  
METİN TOZ
Keyword(s):  
Objective Function ◽  
Artificial Bee Colony ◽  
Image Clustering ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Objective Function Values ◽  
Lion Optimization Algorithm ◽  
Clustering Problems

Thispaperproposesanimprovedformoftheantlionoptimizationalgorithm(IALO)tosolveimageclustering problem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recently proposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and K- means algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies– Bouldin and Xie–Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering.

scholarly journals Performance and evaluation of energy optimization techniques for wireless body area networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Naveen Bilandi ◽  
Harsh Kumar Verma ◽  
Renu Dhir
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Relay Node ◽  
Body Area Networks ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Body Area ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

Abstract Background Wireless body area networks are created to retrieve and transmit human health information by using sensors on the human body. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important mechanisms is selecting the relay node, which can be modeled as an optimization problem. These days nature-inspired algorithms are being widely used to solve various optimization problems. With regard to this, this paper aims to compare the performance of the three most recent nature-inspired metaheuristic algorithms for solving the relay node selection problem. Results It has been found that the total energy consumption calculated using grey wolf optimization decreased by 23% as compared to particle swarm optimization and 16% compared to ant lion optimization. Conclusions The results suggest that grey wolf optimization is better than the other two techniques due to its social hierarchy and hunting behavior. The findings showed that, compared to well-known heuristics such as particle swarm optimization and ant lion optimization, grey wolf optimization was able to deliver extremely competitive results. Graphical Abstract

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.

Test scheduling of System-on-Chip using Dragonfly and Ant Lion optimization algorithms

2020 ◽  
pp. 1-13
Author(s):  
Gokul Chandrasekaran ◽  
P.R. Karthikeyan ◽  
Neelam Sanjeev Kumar ◽  
Vanchinathan Kumarasamy
Keyword(s):  
Ant Colony Optimization ◽  
Artificial Bee Colony ◽  
Optimization Algorithms ◽  
System On Chip ◽  
Test Time ◽  
Test Scheduling ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
On Chip

Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.

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