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Extreme Value Metaheuristics and Coupled Mapped Lattice Approaches for Gas Turbine-Absorption Chiller Optimization

Extreme Value Metaheuristics and Coupled Mapped Lattice Approaches for Gas Turbine-Absorption Chiller Optimization

Timothy Ganesan, Pandian Vasant, Igor Litvinchev, Mohd Shiraz Aris
Copyright: © 2021 |Pages: 30
ISBN13: 9781799839705|ISBN10: 1799839702|ISBN13 Softcover: 9781799850397|EISBN13: 9781799839712
DOI: 10.4018/978-1-7998-3970-5.ch014
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MLA

Ganesan, Timothy, et al. "Extreme Value Metaheuristics and Coupled Mapped Lattice Approaches for Gas Turbine-Absorption Chiller Optimization." Research Advancements in Smart Technology, Optimization, and Renewable Energy, edited by Pandian Vasant, et al., IGI Global, 2021, pp. 283-312. https://doi.org/10.4018/978-1-7998-3970-5.ch014

APA

Ganesan, T., Vasant, P., Litvinchev, I., & Aris, M. S. (2021). Extreme Value Metaheuristics and Coupled Mapped Lattice Approaches for Gas Turbine-Absorption Chiller Optimization. In P. Vasant, G. Weber, & W. Punurai (Eds.), Research Advancements in Smart Technology, Optimization, and Renewable Energy (pp. 283-312). IGI Global. https://doi.org/10.4018/978-1-7998-3970-5.ch014

Chicago

Ganesan, Timothy, et al. "Extreme Value Metaheuristics and Coupled Mapped Lattice Approaches for Gas Turbine-Absorption Chiller Optimization." In Research Advancements in Smart Technology, Optimization, and Renewable Energy, edited by Pandian Vasant, Gerhard Weber, and Wonsiri Punurai, 283-312. Hershey, PA: IGI Global, 2021. https://doi.org/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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