Across neighborhood search for numerical optimization
TLDR
Extensive experiments on 18 benchmark optimization functions of different types show that ANS has well balanced exploration and exploitation capabilities and performs competitively compared with many efficient PBSAs.About:
This article is published in Information Sciences.The article was published on 2016-02-01 and is currently open access. It has received 89 citations till now. The article focuses on the topics: Metaheuristic & Local search (optimization).read more
Citations
More filters
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
Metaheuristic research: a comprehensive survey
TL;DR: A survey of metaheuristic research in literature consisting of 1222 publications from year 1983 to 2016 is performed to highlight potential open questions and critical issues raised in literature and provides guidance for future research to be conducted more meaningfully.
Journal ArticleDOI
Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems
TL;DR: The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems.
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.
References
More filters
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.
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.
Book
Numerical Optimization
Jorge Nocedal,Stephen J. Wright +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
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.
Related Papers (5)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more