Efficient Modeling of Linear Discrete Filters Using Ant Lion Optimizer

Nair, Sreejith S.; Rana, K.P.S.; Kumar, Vineet; Chawla, Abhishek

doi:10.1007/s00034-016-0370-z

Cited by 28 publications

(18 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [194] Nair et al used ALO in order to identify an infinite impulse response(IIR) filters adaptively. They used 4 IIR benchmark with different orders and compared ALO with 2 metaheuristics algorithm GSA and CS.…”

Section: H Other Engineering Applicationsmentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

View full text Add to dashboard Cite

Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & possible future directions that can be investigated.

show abstract

Section: H Other Engineering Applicationsmentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

View full text Add to dashboard Cite

show abstract

“…There have been a few research published in the works about implementing practical optimization or improving the ALO algorithm's performance. Multi-objective optimal generation scheduling (Chopra & Mehta, 2015), optimal nonconvex and dynamic economic load (Kamboj et al, 2017), automatic generation control of interconnected power industry (Gupta & Saxena, 2016), optimal flexible process planning (Petrović & Miljković, 2017), optimal route planning for unmanned aerial vehicle (Yao & Wang, 2017), decisive optimal coefficients of infinite impulse response (IIR) filters (Nair et al 2017), proportional integral derivative (PID) controller parameter design (Saikia & Sinha, 2016), and feature selection problem (Emary & Zawbaa, 2019;Gupta & Saxena, 2016;Zawbaa et al, 2018) are some of them.…”

Section: Introductionmentioning

confidence: 99%

Simulation on Supplier Side Bidding Strategy at Day-ahead Electricity Market Using Ant Lion Optimizer

Jain

Saxena

2022

JCCE

View full text Add to dashboard Cite

In this article, Ant Lion Optimizer (ALO) is used for supplier side optimal bidding strategy in a day-ahead Electricity Market (EM). Optimal bidding is one of the major challenges of EM after deregulation. Deregulation is nothing but abolishing the market rules and unbundling the vertically integrated utilities. In EM, the main objective of Generating Companies (GenCos) is to bid optimally that maximizes its profit. Thus, for attaining maximum profit every supplier makes a strategy for acquiring the profitable bids. The strategic bidding technique for a GenCo in a day-ahead market for multi-hour selling is developed. The challenge of determining the market clearing pricing, load dispatch, and bid cost under three distinct capacities and price blocks is handled by the algorithm using this procedure. In this model, different probability distribution functions are used to explain rivals bidding behavior: normal, lognormal, gamma, and Weibull. Monte Carlo simulations are also carried out. The ALO is applied to maximize the profit of GenCos. The described method was implemented in MATLAB (2019) and evaluated using a standard test case from the literature. The numerical simulations are also displayed and contrasted. It is worth noting that the offered strategy produces the best profit outcomes.

show abstract

“…Following this, Bhatnagar and Gupta [10] in the context of high-order linear time-invariant systems have seen the application of Grey Wolf Optimizer (GWO) towards parametric optimization of their reduced-order models. A comparative study was then attempted by Nair et al [11] in 2017 centering the efficacy of Ant Lion Optimizer (ALO) against alternative reduced-order approaches, including CSA and Gravitational Search Algorithm (GSA).…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

Suid

Ahmad

2022

Automatika

View full text Add to dashboard Cite

The current study introduces the hybridization of the Nonlinear Sine Cosine Algorithm (NSCA) and Safe Experimentation Dynamics (SED) as a novel optimization method for model order reduction of high-order single-input single-output (SISO) systems. Reciprocated synergism between both meta-heuristic algorithms is achieved by appropriating the nonlinear positionupdated mechanism of NSCA for enhanced exploration/exploitation competencies and proficiency of SED in maximizing stagnation avoidance within the local optima. Named the NSCA-SED algorithm, the applicability of the proposed method is assessed by scholastic adoption of a sixth-order numerical transfer function towards two independent high-order systems enclosing Double-Pendulum Overhead Crane and Flexible Manipulator. Experimentation results further suggested NSCA-SED as the superior alternative in terms of execution robustness and consistency excellence against other available optimization-based methods for tackling model order reduction. Exemplified simulations sequentially demonstrated considerable improvements by the employment of NSCA-SED over conventional SCA following respective enhanced proportions of 97.17%, 13.17% and 29.03% for Example 1, Example 2 and Example 3.

show abstract

Efficient Modeling of Linear Discrete Filters Using Ant Lion Optimizer

Cited by 28 publications

References 33 publications

Ant Lion Optimization: Variants, Hybrids, and Applications

Ant Lion Optimization: Variants, Hybrids, and Applications

Simulation on Supplier Side Bidding Strategy at Day-ahead Electricity Market Using Ant Lion Optimizer

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

Contact Info

Product

Resources

About