Timothy Ganesan

Hopfield neural networks approach for design optimization of hybrid power systems with multiple renewable energy sources in a fuzzy environment

The global energy sector faces major challenges in providing sufficient energy to the worlds ever increasing energy demand. Methods to produce a greener, cost effective and reliable source of alternative energy needs to be explored and exploited. One of those methods is done by integrating or hybridizing multiple different alternative energy sources e.g. wind turbine generators, photovoltaic cell panels and fuel-fired generators, equipped with storage batteries to form a distributed generation DG power system. However, even with DG power systems, cost effectiveness, reliability and pollutant emissions are still major issues that need to be resolved. The model development and optimization of the DG power system was carried out successfully in the previous work using Particle Swarm Optimization PSO. The goal was to minimize cost, maximize reliability and minimize emissions multi-objective subject to the requirements of the power balance and design constraints. In this work, due to the uncertain nature on the weather conditions, the power output from the PV cells, WTG and the storage batteries which are subject to insolation and wind conditions were fuzzified in an effort to create a more realistic model. The optimization in a fuzzy environment was then performed by using Hopfield neural network HNN. The optimized results were then discussed and analyzed.

Multiobjective Optimization of Green Sand Mould System Using Chaotic Differential Evolution

Many industrial optimization cases present themselves in a multi-objective (MO) setting (where each of the objectives portrays different aspects of the problem). Therefore, it is important for the decision-maker to have a solution set of options prior to selecting the best solution. In this work, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms; differential evolution (DE), chaotic differential evolution (CDE) and gravitational search algorithm (GSA). These methods are then used to generate the approximate Pareto frontier to the green sand mould system problem. The Hypervolume Indicator (HVI) is applied to gauge the capabilities of each algorithm in approximating the Pareto frontier. Some comparative studies were then carried out with the algorithms developed in this work and that from the previous work. Analysis on the performance as well as the quality of the solutions obtained by these algorithms is shown here.

Hopfield differential evolution for multi-objective optimization of a cement-bonded sand mould system

Frequently, optimization cases in engineering and other heavy industries present themselves in a multi-objective setting. Thus, it would greatly aid the decision maker if a series of multiple solutions were at hand prior to selecting the suitable solution. In this work, the weighted sum scalarization approach was used in conjunction with the Differential Evolution (DE) and the improved Hopfield Differential Evolution (Hopf-DE) algorithm. The DE and Hopf-DE approaches were then applied to the cement-bonded sand mould system to construct the approximate Pareto frontier, which was then employed to identify the best solution option. Some analysis was then performed on the computational results produced. Examination of the performance and the quality of the solutions obtained by the DE and Hopf-DE algorithms is presented. In addition, an analysis of the degree of conflict between the two objective functions with respect to the search space was carried out. The findings from this analysis are discussed in this paper.

An Algorithmic Framework for Multiobjective Optimization

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

Cuckoo Optimization Algorithm for Optimal Power Flow

This paper proposes a cuckoo optimization algorithm (COA) method for solving optimal power flow (OPF) problem. The proposed method is inspired from the life of the family of cuckoo. In the proposed method, there are two main components including mature cuckoos and cuckoo’s eggs. During the survival competition, the survived cuckoo societies immigrate to a better environment and restart the process. The cuckoo’s survival effort hopefully converges to a state that there is only one cuckoo society with the same maximum profit values. The COA method has been tested on the IEEE 30, 57, and 118- bus systems with three kinds of objective function and the obtained results have been compared to those from conventional particle swarm optimization (PSO) method. The result comparison has shown that the proposed method can obtain better optimal solution than the conventional PSO. Therefore, the proposed COA could be a useful method for implementation in solving the OPF problem.

Swarm intelligence for multi-objective optimization of synthesis gas production

In the chemical industry, the production of methanol, ammonia, hydrogen and higher hydrocarbons require synthesis gas (or syn gas). The main three syn gas production methods are carbon dioxide reforming (CRM), steam reforming (SRM) and partial-oxidation of methane (POM). In this work, multi-objective (MO) optimization of the combined CRM and POM was carried out. The empirical model and the MO problem formulation for this combined process were obtained from previous works. The central objectives considered in this problem are methane conversion, carbon monoxide selectivity and the hydrogen to carbon monoxide ratio. The MO nature of the problem was tackled using the Normal Boundary Intersection (NBI) method. Two techniques (Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO)) were then applied in conjunction with the NBI method. The performance of the two algorithms and the quality of the solutions were gauged by using two performance metrics. Comparative studies and results analysis ...

Design optimization of a fuzzy distributed generation (DG) system with multiple renewable energy sources

The global rise in energy demands brings major obstacles to many energy organizations in providing adequate energy supply. Hence, many techniques to generate cost effective, reliable and environmentally friendly alternative energy source are being explored. One such method is the integration of photovoltaic cells, wind turbine generators and fuel-based generators, included with storage batteries. This sort of power systems are known as distributed generation (DG) power system. However, the application of DG power systems raise certain issues such as cost effectiveness, environmental impact and reliability. The modelling as well as the optimization of this DG power system was successfully performed in the previous work using Particle Swarm Optimization (PSO). The central idea of that work was to minimize cost, minimize emissions and maximize reliability (multi-objective (MO) setting) with respect to the power balance and design requirements. In this work, we introduce a fuzzy model that takes into account the uncertain nature of certain variables in the DG system which are dependent on the weather conditions (such as; the insolation and wind speed profiles). The MO optimization in a fuzzy environment was performed by applying the Hopfield Recurrent Neural Network (HNN). Analysis on the optimized results was then carried out.The global rise in energy demands brings major obstacles to many energy organizations in providing adequate energy supply. Hence, many techniques to generate cost effective, reliable and environmentally friendly alternative energy source are being explored. One such method is the integration of photovoltaic cells, wind turbine generators and fuel-based generators, included with storage batteries. This sort of power systems are known as distributed generation (DG) power system. However, the application of DG power systems raise certain issues such as cost effectiveness, environmental impact and reliability. The modelling as well as the optimization of this DG power system was successfully performed in the previous work using Particle Swarm Optimization (PSO). The central idea of that work was to minimize cost, minimize emissions and maximize reliability (multi-objective (MO) setting) with respect to the power balance and design requirements. In this work, we introduce a fuzzy model that takes into account ...

Game-theoretic differential evolution for multiobjective optimization of green sand mould system

Many large-scale engineering problems often take a multiobjective form. Thus, several solution options to the MO problem are usually ascertained by the engineer. Then the most desirable options with respect to the industrial circumstances and online operating conditions are selected. In this work, the trade-off solutions are obtained using the weighted-sum approach. In addition the standard metaheuristic, differential evolution is improved using concepts from evolutionary game theory. These techniques are then applied to solve the industrial green sand mould development problem. The solutions are then examined and discussed from various standpoints.

Multiobjective Optimization of Bioactive Compound Extraction Process via Evolutionary Strategies

Systematic and simultaneous optimization of a collection of objectives is called multiobjective or multicriteria optimization. These sorts of optimization procedures are becoming commonplace in fields involving engineering design, process and system optimization. In this work, the multiobjective (MO) optimization of the bioactive compound extraction process was carried out. Using the Normal Boundary Intersection (NBI) approach the MO optimization problem is transformed into a weighted form called the beta-subproblem. This subproblem is then solved using two evolutionary strategies (differential evolution (DE) and genetic algorithm (GA)). Using these evolutionary strategies, the solutions to the extraction process which form the efficient Pareto frontier was generated. The Hypervolume Indicator (HVI) was applied to the solutions to rank the strategies based on the solution quality. Critical analyses and comparative studies were then carried out on the strategies employed in this work and that from the previous work.

Development of a modular general purpose controller board for biologically inspired robot

This paper describes the development of a modular general purpose controller board for biologically inspired robot. The circuits are designed using Protel, an electronic design software that bring together the traditionally separate disciplines of board design, programmable hardware design and software development. The general purpose controller board which currently has five categories; power, communication, microcontroller, inputs, and outputs, is divided into five different modular boards. The design, development and evaluation of the general purpose controller board is found to be able to meet the requirements of biologically inspired robots.