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2017 | OriginalPaper | Chapter

An Improved Multi-objective Bare-Bones PSO for Optimal Design of Solar Dish Stirling Engine Systems

Authors : Qun Niu, Ziyuan Sun, Dandan Hua

Published in: Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration

Publisher: Springer Singapore

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Abstract

An improved bare-bones multi-objective particle swarm optimization, namely IMOBBPSO is proposed to optimize the solar-dish Stirling engine systems. A new simple strategy for updating particle’s velocity is developed based on the conventional bare-bones PSO, aiming to enhance the diversity of the solutions and accelerate the convergence rate. In order to test the effectiveness of IMOBBPSO, four benchmarks are used. Compared with the non-dominated sorting genetic algorithm-II (NSGAII) and multi-objective particle swarm optimization algorithm (MOPSO), it is revealed that IMOBBPSO can quickly converge to the true Pareto front and efficiently solve practical problems. IMOBBPSO is then used to solve the design of the solar-dish Stirling engine. It is shown that IMOBBPSO obtains the best optimization results than NSGAII and MOPSO. It further achieves significant improvements 25.6102% to 29.2926% in terms of the output power and entropy generation rate when it is compared with existing results in the literature.

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Metadata
Title
An Improved Multi-objective Bare-Bones PSO for Optimal Design of Solar Dish Stirling Engine Systems
Authors
Qun Niu
Ziyuan Sun
Dandan Hua
Copyright Year
2017
Publisher
Springer Singapore
Book
Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration

Print ISBN: 978-981-10-6363-3

Electronic ISBN: 978-981-10-6364-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-6364-0_17

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