Lévy-enhanced swarm intelligence for optimizing a multiobjective biofuel supply chain
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 engineerin...
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| Main Authors: | , |
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| Format: | Book |
| Published: |
IGI Global
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125612780&doi=10.4018%2f978-1-7998-1216-6.ch012&partnerID=40&md5=ef3cbc8f1a83c21ce264538a86850308 http://eprints.utp.edu.my/30141/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | 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. © 2020, IGI Global. |
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