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Erschienen in:

2016 | OriginalPaper | Buchkapitel

Multi-objective Colliding Bodies Optimization

verfasst von : Arnapurna Panda, Sabyasachi Pani

Erschienen in: Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Verlag: Springer Singapore

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Abstract

Kaveh and Mahdavi proposed a new metaheuristic method in 2014 known as colliding bodies optimization (CBO). The algorithm is based on the principle of collision between bodies (each has a specific mass and velocity). The collision makes the bodies move toward the optimum position in the search space. This paper deals with the multi-objective formulation of CBO termed as MOCBO. Simulation studies on benchmark functions Schaffer N1, Schaffer N2, and Kursawe have demonstrated the superior performance of the MOCBO over multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance analysis are carried out for the proposed and benchmark algorithms in identical platforms using response matching between obtained and true Pareto front; the convergence matric, diversity matric, and computational efficiency achieved over fifty independent runs.

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Titel: Multi-objective Colliding Bodies Optimization
verfasst von: Arnapurna Panda
Sabyasachi Pani
Verlag: Springer Singapore
Buch: Proceedings of Fifth International Conference on Soft Computing for Problem Solving
Print ISBN: 978-981-10-0447-6

Electronic ISBN: 978-981-10-0448-3

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-981-10-0448-3_54

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