Archive | 2019
Examples of Enhancing Optimization Algorithms
Abstract
The aim of this chapter is to show how to develop a new hybrid real-coded genetic algorithm (RCGA) and an enhanced differential evolution (DE) to identify soil parameters. As first example, under the framework of a classical GA, the hybrid RCGA is developed by combining two recently developed and efficient crossover operators with a hybrid strategy. A dynamic random mutation has also been incorporated into the new RCGA to maintain the diversity of the population. Additionally, in order to improve the convergence speed, a chaotic local search (CLS) has been adopted. Then, for the second example, an enhanced DE was developed by implementing the Nelder–Mead simplex method in a differential algorithm in order to accelerate the convergence speed with strong reliable search ability. The performance of both improved optimization algorithms is highlighted by identifying model parameters from pressuremeter tests and excavation observations.