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Extreme Value Metaheuristics and Coupled Mapped Lattice Approaches for Gas Turbine-Absorption Chiller Optimization
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Author(s): Timothy Ganesan (RS Energy, Calgary, Canada), Pandian Vasant (University of Technology Petronas, Malaysia), Igor Litvinchev (Nuevo Leon State University, Mexico)and Mohd Shiraz Aris (TNB Research, Malaysia)
Copyright: 2021
Pages: 30
Source title:
Research Advancements in Smart Technology, Optimization, and Renewable Energy
Source Author(s)/Editor(s): Pandian Vasant (University of Technology Petronas, Malaysia), Gerhard Weber (Poznan University of Technology, Poland)and Wonsiri Punurai (Mahidol University, Thailand)
DOI: 10.4018/978-1-7998-3970-5.ch014
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Abstract
The increasing complexity of engineering systems has spurred the development of highly efficient optimization techniques. This chapter focuses on two novel optimization methodologies: extreme value stochastic engines (random number generators) and the coupled map lattice (CML). This chapter proposes the incorporation of extreme value distributions into stochastic engines of conventional metaheuristics and the implementation of CMLs to improve the overall optimization. The central idea is to propose approaches for dealing with highly complex, large-scale multi-objective (MO) problems. In this work the differential evolution (DE) approach was employed (incorporated with the extreme value stochastic engine) while the CML was employed independently (as an analogue to evolutionary algorithms). The techniques were then applied to optimize a real-world MO Gas Turbine-Absorption Chiller system. Comparative analyses among the conventional DE approach (Gauss-DE), extreme value DE strategies, and the CML were carried out.
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