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Grey Wolf Optimizer

Seyedali Mirjalili, +2 more
- 01 Mar 2014 - 
- Vol. 69, Iss: 69, pp 46-61
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TLDR
The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
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This article is published in Advances in Engineering Software.The article was published on 2014-03-01 and is currently open access. It has received 10082 citations till now. The article focuses on the topics: Evolutionary programming & Metaheuristic.

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The Whale Optimization Algorithm

Seyedali Mirjalili, +1 more
- 01 May 2016 - 
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.
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Salp Swarm Algorithm

Seyedali Mirjalili, +5 more
- 01 Dec 2017 - 
TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
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Moth-flame optimization algorithm

Seyedali Mirjalili
- 01 Nov 2015 - 
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.
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Harris hawks optimization: Algorithm and applications

Ali Asghar Heidari, +6 more
- 01 Aug 2019 - 
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

The Ant Lion Optimizer

Seyedali Mirjalili
- 01 May 2015 - 
TL;DR: The results of the test functions prove that the proposed ALO algorithm is able to provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence, showing that this algorithm has merits in solving constrained problems with diverse search spaces.
References
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Journal ArticleDOI

Optimization by Simulated Annealing

Scott Kirkpatrick, +2 more
- 13 May 1983 - 
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.
Proceedings ArticleDOI

Particle swarm optimization

James Kennedy, +1 more
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, +1 more
- 01 Dec 1997 - 
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

Genetic Algorithms

David E. Goldberg, +1 more
Journal ArticleDOI

No free lunch theorems for optimization

David H. Wolpert, +1 more
- 01 Apr 1997 - 
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)

The Whale Optimization Algorithm

Seyedali Mirjalili, +1 more
- 01 May 2016 - 

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Rainer Storn, +1 more
- 01 Dec 1997 - 

GSA: A Gravitational Search Algorithm

Esmat Rashedi, +2 more
- 01 Jun 2009 - 

No free lunch theorems for optimization

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Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "Grey wolf optimizer author" ?

This work proposes a new meta-heuristic called Grey Wolf Optimizer ( GWO ) inspired by grey wolves ( Canis lupus ). The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization ( PSO ), Gravitational Search Algorithm ( GSA ), Differential Evolution ( DE ), Evolutionary Programming ( EP ), and Evolution Strategy ( ES ). The results show that the GWO algorithm is able to provide very competitive results compared to these wellknown meta-heuristics. The paper also considers solving three classical engineering design problems ( tension/compression spring, welded beam, and pressure vessel designs ) and presents a real application of the proposed method in the field of optical engineering.