Intelligent Techniques for Photocatalytic Removal of Pollution in Wastewater

This paper discusses the elimination of C.I. AY23 (Acid Yellow 23) using UV/Ag-TiO2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO2 nanoparticles processed under desired circumstances, two computational techniques namely NN (neural network) and PSO (particle swarm optimization) modeling are developed. A summed up of 100 data are used to establish the models, wherein introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO2 and irradiation time are the four parameters applied as the input variables and elimination of AY23 as the output variable. The comparison among the predicted results by designed models and the experimental data proves that the performance of the NN model is comparatively sophisticated than the PSO model

A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm

This paper develops a particle swarm optimisation (PSO) based framework for multi-objective optimisation (MOO). As a part of development, a new PSO method, named self-adaptive PSO (SAPSO), is first proposed. Since the convergence of SAPSO determines the quality of the obtained Pareto front, this paper analytically investigates the convergence of SAPSO and provides a parameter selection principle that guarantees the convergence. Leveraging the proposed SAPSO, this paper then designs a SAPSO-based MOO framework, named SAMOPSO. To gain a well-distributed Pareto front, we also design an external repository that keeps the non-dominated solutions. Next, a circular sorting method, which is integrated with the elitist-preserving approach, is designed to update the external repository in the developed MOO framework. The performance of the SAMOPSO framework is validated through 12 benchmark test functions and a real-word MOO problem. For rigorous validation, the performance of the proposed framework is compared with those of four well-known MOO algorithms. The simulation results confirm that the proposed SAMOPSO outperforms its contenders with respect to the quality of the Pareto front over the majority of the studied cases. The non-parametric comparison results reveal that the proposed method is significantly better than the four algorithms compared at the confidence level of 90% over the 12 test functions

Penentuan Lokasi dan Kapasitas Distributed Generation (DG) Optimal pada Sistem Distribusi Radial Aktif Menggunakan Metode Multi-Objective Particle Swarm Optimization (MOPSO) Berbasis Decision Support System (DSS)

Kebutuhan listrik dunia saat ini semakin meningkat, Hal ini dipengaruhi oleh meningkatnya populasi manusia, proses urbanisasi, dan perkembangan yang luas di sektor industri. Dengan meningkatnya kebutuhan listrik berakibat pada meningkatnya rugi-rugi daya, susut tegangan (drop tegangan) dan menurunkan kapabilitas dari jaringan distribusi. Solusi untuk permasalahan ini dapat diatasi dengan salah satunya dengan memasangan Distributed Generation (DG). Dengan pemasangan Distributed Generation (DG) pada sistem distribusi radial aktif dapat mengurangi rugi-rugi daya pada saluran, mengurangi susut tegangan (drop tegangan) dan meningkatkan kapabilitas jaringan sistem distribusi. Oleh karena itu, pada tugas akhir ini akan direncanakan dan disimulasikan peletakan lokasi dan penentuan kapasitas Distributed Generation (DG) yang optimal dengan menggunakan metode Multi-Objective Particle Swarm Optimization (MOPSO) dengan mempertimbangkan rugi-rugi daya listrik dan deviasi tegangan berbasis aplikasi Decision Support System (DSS). Dengan dilakukan penempatan single DG hingga multi DG maka didapatkan hasil, pada Sistem IEEE 33 Bus dengan nilai rugi-rugi daya awal adalah 202.7 KW menjadi 12.153 KW dengan pemasangan 3 DG dan paada sistem Penyulang Basuki Rahmat nilai Rugi-rugi daya awal adalah 25.38 kW menjadi 0.819 KW dengan pemasangan 2 DG. ========================================================================================================= The current world’s electricity demand is increasing, it is influenced by the increasing of human population, the process of urbanization, and the wide development in industrial sector. With the increasing demand for electricity resulting in increased loss of power, voltage loss (voltage drop) and lower capability of the distribution network. The solution to this problem can be solved by installing Distributed Generation (DG). With the installation of Distributed Generation (DG) on the active radial distribution system can reduce the power losses on the channel, reduce the voltage loss (voltage drop) and increase network distribution system capabilities. Therefore, in this final project will be planned and simulated the optimal placement location and sizing of Distributed Generation (DG) by using Multi-Objective Particle Swarm Optimization (MOPSO) method with considering the loss of electric power and voltage deviation, based on application of Decision Support System (DSS). With the placement of single DG to multi DG then the results obtained, on IEEE 33 Bus System with initial power loss value is 202.7 KW reduce to be 12.153 KW with installation of 3 DG and on Basuki Rahmat system initial power loss is 25.38 kW reduce to be 0.819 KW with 2 DG installation

Evolving Fault Tolerant Robotic Controllers

Fault tolerant control and evolutionary algorithms are two different research areas. However with the development of artificial intelligence, evolutionary algorithms have demonstrated competitive performance compared to traditional approaches for the optimisation task. For this reason, the combination of fault tolerant control and evolutionary algorithms has become a new research topic with the evolving of controllers so as to achieve different fault tolerant control schemes. However most of the controller evolution tasks are based on the optimisation of controller parameters so as to achieve the fault tolerant control, so structure optimisation based evolutionary algorithm approaches have not been investigated as the same level as parameter optimisation approaches. For this reason, this thesis investigates whether structure optimisation based evolutionary algorithm approaches could be implemented into a robot sensor fault tolerant control scheme based on the phototaxis task in addition to just parameter optimisation, and explores whether controller structure optimisation could demonstrate potential benefit in a greater degree than just controller parameter optimisation. This thesis presents a new multi-objective optimisation algorithm in the structure optimisation level called Multi-objective Cartesian Genetic Programming, which is created based on Cartesian Genetic Programming and Non-dominated Sorting Genetic Algorithm 2, in terms of NeuroEvolution based robotic controller optimisation. In order to solve two main problems during the algorithm development, this thesis investigates the benefit of genetic redundancy as well as preserving neutral genetic drift in order to solve the random neighbour pick problem during crowding fill for survival selection and investigates how hyper-volume indicator is employed to measure the multi-objective optimisation algorithm performance in order to assess the convergence for Multi-objective Cartesian Genetic Programming. Furthermore, this thesis compares Multi-objective Cartesian Genetic Programming with Non-dominated Sorting Genetic Algorithm 2 for their evolution performance and investigates how Multi-objective Cartesian Genetic Programming could be performing for a more difficult fault tolerant control scenario besides the basic one, which further demonstrates the benefit of utilising structure optimisation based evolutionary algorithm approach for robotic fault tolerant control