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Erschienen in:
Gbest-Guided Covariance Matrix Adaptation Evolution Strategy for Large Scale Global Optimization Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Whale Swarm Algorithm for Function Optimization

verfasst von : Bing Zeng, Liang Gao, Xinyu Li

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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Abstract

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper proposes a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired from the whales’ behavior of communicating with each other via ultrasound for hunting. The proposed Whale Swarm Algorithm is compared with several popular metaheuristic algorithms on comprehensive performance metrics. According to the experimental results, Whale Swarm Algorithm has a quite competitive performance when compared with other algorithms.

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Vorheriges Kapitel Univariate Gaussian Model for Multimodal Inseparable Problems
Nächstes Kapitel The Container Truck Route Optimization Problem by the Hybrid PSO-ACO Algorithm
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Metadaten
Titel
Whale Swarm Algorithm for Function Optimization
verfasst von
Bing Zeng
Liang Gao
Xinyu Li
Copyright-Jahr
2017
Buch
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-63308-4

Electronic ISBN: 978-3-319-63309-1

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-63309-1_55

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