Journal ArticleDOI
Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems
TLDR
An efficient optimization method called 'Teaching-Learning-Based Optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions.About:
This article is published in Information Sciences.The article was published on 2012-01-01. It has received 1359 citations till now. The article focuses on the topics: Continuous optimization & Engineering optimization.read more
Citations
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Journal ArticleDOI
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
Journal ArticleDOI
Harris hawks optimization: Algorithm and applications
Ali Asghar Heidari,Ali Asghar Heidari,Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah,Majdi Mafarja,Huiling Chen +6 more
TL;DR: The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques.
Journal ArticleDOI
Slime mould algorithm: A new method for stochastic optimization
Shimin Li,Huiling Chen,Mingjing Wang,Ali Asghar Heidari,Ali Asghar Heidari,Seyedali Mirjalili +5 more
TL;DR: The proposed slime mould algorithm has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity.
Journal ArticleDOI
Aquila Optimizer: A novel meta-heuristic optimization algorithm
Laith Abualigah,Dalia Yousri,Mohamed Abd Elaziz,Ahmed A. Ewees,Mohammed A. A. Al-qaness,Amir H. Gandomi +5 more
TL;DR: From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed.
Journal ArticleDOI
Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications
TL;DR: The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors, and the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Journal ArticleDOI
GSA: A Gravitational Search Algorithm
TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.