Extreme Value Metaheuristics for Optimizing a Many-Objective Gas Turbine System

Abstract

The increasing complexity of engineering systems has spurred the development of highly efficient optimization techniques. Stochastic engines or random number generators are commonly used to initialize metaheuristic approaches. This article proposes the incorporation of extreme value distribution into stochastic engines to improve the performance of the optimization technique. The central idea is to propose a potential boost to optimization algorithms for dealing with highly complex problems. In this article, the differential evolution (DE) approach is employed. Using two extreme value distributions, two DE variants are developed by modifying their stochastic engines: Pareto-DE and Extreme-DE. The algorithms are then applied to optimize a complex multiobjective (MO) Gas Turbine – Absorption Chiller system. Comparative analyses against the conventional DE approach (Gauss-DE) and a detail discussion on the optimization results are carried out.