Journal ArticleDOI
Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique
TLDR
MFO algorithm is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation.About:
This article is published in Applied Soft Computing.The article was published on 2017-10-01. It has received 184 citations till now. The article focuses on the topics: AC power.read more
Citations
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Journal ArticleDOI
Enhanced Moth-flame optimizer with mutation strategy for global optimization
TL;DR: GM is introduced into the basic MFO to improve neighborhood-informed capability, CM with a large mutation step is adopted to enhance global exploration ability and LM is embedded to increase the randomness of search agents’ movement.
Journal ArticleDOI
Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems
TL;DR: A novel bio-inspired optimization algorithm namely the Barnacles Mating Optimizer (BMO) algorithm to solve optimization problems that mimics the mating behaviour of barnacles in nature for solving optimization problems.
Journal ArticleDOI
An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks
Yueting Xu,Huiling Chen,Ali Asghar Heidari,Ali Asghar Heidari,Jie Luo,Qian Zhang,Xuehua Zhao,Chengye Li +7 more
TL;DR: The proposed CLSGMFO can serve as an effective and efficient computer-aided tool for financial prediction and demonstrate that the proposed learning scheme can offer a superior kernel extreme learning machine model with excellent predictive performance.
Journal ArticleDOI
A novel hybrid model for short-term wind power forecasting
TL;DR: An improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data to enhance prediction accuracy.
Journal ArticleDOI
Moth–flame optimization algorithm: variants and applications
Mohammad Shehab,Laith Abualigah,Husam Al Hamad,Hamzeh Alabool,Mohammad Alshinwan,Ahmad M. Khasawneh +5 more
TL;DR: This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics, focusing on the current work on MFO, highlight its weaknesses, and suggest possible future research directions.
References
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Journal ArticleDOI
No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Book
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
TL;DR: This volume explores the differential evolution (DE) algorithm in both principle and practice and is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
Journal ArticleDOI
MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education
TL;DR: The details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture, are presented, which are used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits.
Book
Differential Evolution: A Practical Approach to Global Optimization
TL;DR: The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast as discussed by the authors, which is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimisation.
Journal ArticleDOI
Moth-flame optimization algorithm
TL;DR: The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems and the statistical results show that this algorithm is able to provide very promising and competitive results.