Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Journal of Global Optimization
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Wasp Swarm Algorithm for Dynamic MAX-SAT Problems
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A Novel Global Convergence Algorithm: Bee Collecting Pollen Algorithm
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Uncertainty Analysis and Decision Making; Guest Editors: Yan-Kui Liu, Baoding Liu, Jinwu Gao
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
A Dolphin Partner Optimization
GCIS '09 Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01
A new optimization method: Big Bang-Big Crunch
Advances in Engineering Software
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Nature-Inspired Metaheuristic Algorithms: Second Edition
Nature-Inspired Metaheuristic Algorithms: Second Edition
ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization
Expert Systems with Applications: An International Journal
International Journal of Computational Science and Engineering
Small-World optimization algorithm for function optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example
Knowledge-Based Systems
IEEE Computational Intelligence Magazine
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
A new meta-heuristic method: Ray Optimization
Computers and Structures
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Let a biogeography-based optimizer train your Multi-Layer Perceptron
Information Sciences: an International Journal
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This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. 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 well-known 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. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.