A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum

DOI10.1016/j.amc.2012.06.078zbMath1291.90187OpenAlexW2060181508WikidataQ57705314 ScholiaQ57705314MaRDI QIDQ2250167

Ömer Kaan Baykan, Mesut Gündüz, Mustafa Servet Kıran

Publication date: 4 July 2014

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2012.06.078

zbMATH Keywords

global minimumant colony optimizationparticle swarm optimizationhybrid metaheuristic

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (5)

A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean ⋮ An improved particle swarm optimization algorithm based on centroid and exponential inertia weight ⋮ CGRS -- an advanced hybrid method for global optimization of continuous functions closely coupling extended random search and conjugate gradient method ⋮ The hop-constrained minimum cost flow spanning tree problem with nonlinear costs: an ant colony optimization approach ⋮ A new hybrid algorithm for continuous optimization problem

Cites Work

• Unnamed Item
• Ant colony optimization for continuous domains
• Global optimizations and tabu search based on memory
• Particle swarm and ant colony algorithms hybridized for improved continuous optimization
• Continuous functions minimization by dynamic random search technique
• Improved particle swarm optimization combined with chaos
• A heuristic approach for finding the global minimum: Adaptive random search technique
• Ant colony optimization for finding the global minimum

This page was built for publication: A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum