Load Distribution of Evolutionary Algorithm for Complex-Process Optimization Based on Differential Evolutionary Strategy in Hot Rolling Process

Prediction and optimization of load and torque in ring rolling process through development of artificial neural network and evolutionary algorithms

Developing artificial neural network (ANN), a model to make a correct prediction of required force and torque in ring rolling process is developed for the first time. Moreover, an optimal state of process for specific range of input parameters is obtained using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Radii of main roll and mandrel, rotational speed of main roll, pr...

An evolutionary method for complex-process optimization

In this paper we present a new evolutionary method for complex-process optimization. It is partially based on principles of the scatter search methodology, but it makes use of innovative strategies to be more effective in the context of complex-process optimization using a small number of tuning parameters. In particular, we introduce a new combination method based on path relinking, which cons...

Developing Adaptive Differential Evolution as a New Evolutionary Algorithm, Application in Optimization of Chemical Processes

Evolutionary Distance Model Based on Differential Equation and Markov Process

Evolutionary distance is a measure of evolutionary divergence time between two homologous sequences. It is fundamental for the study of molecular evolution. In this paper, we exploit one-parameter model and two-substitution model using the differential equation and the Markov process in stochastic process. Through them, we can acquire the evolutionary distance between two sequences as estimated...

Optimization in Uncertain and Complex Dynamic Environments with Evolutionary Methods

In the real world, many of the optimization issues are dynamic, uncertain, and complex in which the objective function or constraints can be changed over time. Consequently, the optimum of these issues is changed nonlinearly. Therefore, the optimization algorithms not only should search the global optimum value in the space but also should follow the path of optimal change in dynamic environmen...