Journal ArticleDOI
Modern meta-heuristics based on nonlinear physics processes: A review of models and design procedures
TLDR
The most important optimization algorithms based on nonlinear physics, how they have been constructed from specific modeling of a real phenomena, and also their novelty in terms of comparison with alternative existing algorithms for optimization are reviewed.About:
This article is published in Physics Reports.The article was published on 2016-10-19. It has received 125 citations till now. The article focuses on the topics: Metaheuristic & Evolutionary computation.read more
Citations
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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
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
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method
Iman Ahmadianfar,Ali Asghar Heidari,Ali Asghar Heidari,Amir H. Gandomi,Xuefeng Chu,Huiling Chen +5 more
TL;DR: This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics.
Journal ArticleDOI
Bio-inspired computation: Where we stand and what's next
Javier Del Ser,Javier Del Ser,Eneko Osaba,Daniel Molina,Xin-She Yang,Sancho Salcedo-Sanz,David Camacho,Swagatam Das,Ponnuthurai Nagaratnam Suganthan,Carlos A. Coello Coello,Francisco Herrera +10 more
TL;DR: The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
Proceedings ArticleDOI
Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems
TL;DR: Numerical results and non-parametric statistical significance tests indicate that the Coyote Optimization Algorithm is capable of locating promising solutions and it outperforms other metaheuristics on most tested functions.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
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.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Related Papers (5)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more