(PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The feasibility of the proposed method is demonstrated and compared with Genetic Algorithms (GAs) based image enhancement technique. The obtained results indicate that the proposed PSO yields better results in terms of both the maximization of the number of pixels in the edges and the adopted objective evaluation. Computational time is also relatively small in the PSO case compared to the GA case.

Abstract. In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in modeling a chemical process. This is particularly useful for cases involving changing operating conditions as well as highly nonlinear processes. As a case study, a Tennessee Eastman (TE) chemical process reactor was considered. Four subsystems of the reactor were considered. They are the reactor level, the reactor pressure, the reactor cooling water temperature, and the reactor temperature. PSO is proposed to allow automatic update of network weights to increase the adaptability to dynamic environment. Comparisons were also made to training the ANN using Genetic Algorithms (GAs). The results obtained in this paper confirmed the potential of PSO-based ANN model to successfully model the TE process. The results are explored with a discussion using the Mean Square Error (MSE) and Variance-Account-For (VAF) to illustrate the usability of the proposed approach. Finally, conclusions and future works are derived.