Lévy-Enhanced Swarm Intelligence for Optimizing a Multiobjective Biofuel Supply Chain

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Author(s): T. Ganesan (Royal Bank of Canada, Toronto, Canada)and Pandian Vasant (Universiti Teknologi PETRONAS, Perak, Malaysia)
Copyright: 2020
Pages: 23
Source title: Handbook of Research on Smart Computing for Renewable Energy and Agro-Engineering
Source Author(s)/Editor(s): Valeriy Kharchenko (Federal Scientific Agroengineering Center VIM, Russia)and Pandian Vasant (Universiti Teknologi Petronas, Malaysia)
DOI: 10.4018/978-1-7998-1216-6.ch012

Keywords: Agricultural & Food Technologies / Agricultural and Food Technologies / Environment & Agriculture / Information Science Reference

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Abstract

Engineering systems are currently plagued by various complexities and uncertainties. Metaheuristics have emerged as an essential tool for effective engineering design and operations. Nevertheless, conventional metaheuristics still struggle to reach optimality in the face of highly complex engineering problems. Aiming to further boost the performance of conventional metaheuristics, strategies such as hybridization and various enhancements have been added into the existing solution methods. In this work, swarm intelligence techniques were employed to solve the real-world, large-scale biofuel supply chain problem. Additionally, the supply chain problem considered in this chapter is multiobjective (MO) in nature. Comparative analysis was then performed on the swarm techniques. To further enhance the search capability of the best solution method (GSA), the Lévy flight component from the Cuckoo Search (CS) algorithm was incorporated into the Gravitational Search Algorithm (GSA) technique; developing the novel Lévy-GSA technique. Measurement metrics were then utilized to analyze the results.

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Lévy-Enhanced Swarm Intelligence for Optimizing a Multiobjective Biofuel Supply Chain

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