KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules

Swarm-based algorithms have acquired an important role in solving real-world optimization problems. In this paper, Kinetic Gas Molecule Optimization (KGMO), an optimization algorithm that is based on the kinetic energy of gas molecules, is introduced. The agents are gas molecules that are moving in...

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Bibliographic Details
Published in:Information sciences Vol. 275; pp. 127 - 144
Main Authors: Moein, Sara, Logeswaran, Rajasvaran
Format: Journal Article
Language:English
Published: Elsevier Inc 10.08.2014
Subjects:
Algorithms
Benchmarking
Computer simulation
Gravitational Search Algorithm (GSA)
Kinetic energy
Kinetic Gas Molecule Optimization (KGMO)
Mathematical models
Mean square values
Optimal convergence
Optimization
Particle Swarm Optimization (PSO)
Swarm intelligence
Kinetic Gas Molecule Optimization (KGMO)
Optimal convergence
Kinetic energy
Gravitational Search Algorithm (GSA)
Particle Swarm Optimization (PSO)
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:Swarm-based algorithms have acquired an important role in solving real-world optimization problems. In this paper, Kinetic Gas Molecule Optimization (KGMO), an optimization algorithm that is based on the kinetic energy of gas molecules, is introduced. The agents are gas molecules that are moving in the search space; they are subject to the kinetic theory of gases, which defines the rules for gas molecule interactions in the model. The performance of the proposed algorithm, in terms of its ability to find the global minima of 23 nonlinear benchmark functions, is evaluated against the corresponding results of two well-known benchmark algorithms, namely, Particle Swarm Optimization (PSO) and the recently developed high-performance Gravitational Search Algorithm (GSA). The simulations that were undertaken indicate that KGMO achieves better results in decreasing the Mean Square Error (MSE). Significant improvements of up to 107 and 1020 times were achieved by KGMO against PSO and GSA, respectively, in solving unimodal benchmark functions within 150 iterations. Improvements of at least tenfold were achieved in solving the multimodal benchmark functions. The proposed algorithm is more accurate and converges faster than does the benchmark algorithms, which makes this algorithm especially useful in solving complex optimization problems.
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ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2014.02.026