Studies in particle swarm optimization technique for global optimization.

Ph. D. University of KwaZulu-Natal, Durban 2013.Abstract available in the digital copy.Articles found within the main body of the thesis in the print version is found at the end of the thesis in the digital version

APPROXIMATED UNIMODAL REGION ELIMINATION BASED GLOBAL OPTIMIZATION METHOD FOR ENGINEERING DESIGN

ABSTRACT Computer analysis and simulation based design optimization requires more computationally efficient global optimization tools. In this work, a new global optimization algorithm based on design experiments, region elimination and response surface model, namely Approximated Unimodal Region Elimination Method (AUREM), is introduced. The approach divides the field of interest into several unimodal regions using design experiment data; identify and rank the regions that most likely contain the global minimum; form a response surface model with additional design experiment data over the most promising region; identify its minimum, remove this processed region, and move to the next most promising region. By avoiding redundant searches, the approach identifies the global optimum with reduced number of objective function evaluations and computation effort. The new algorithm was tested using a variety of benchmark global optimization problems and compared with several widely used global optimization algorithms. The experiments results present comparable search accuracy and superior computation efficiency, making the new algorithm an ideal tool for computer analysis and simulation black-box based global design optimization. INTRUDUCTION Background With the rapid advances in Computer Aided Design, Engineering and Manufacturing (CAD/CAE/CAM), virtual prototyping of a new design using computer modeling, analysis and simulation tools has become more common. The computational function modules in CAD/CAE/CAM, including finite element analysis (FEA), computational fluid dynamics (CFD), kinematics/dynamics analysis, motion animation and CNC tool path simulation, automatically evaluate and accurately predict the performance of a mechanical design. It is quite natural to further extend the practice to allow design optimizations be carried out using these virtual-prototyping black-boxes as the objective and constraint functions. These optimizations are used to identify the best combination of design parameters in the complex; black-box based multidisciplinary design problems. However, this type of optimizations often has non-unimodal objective function and non-convex feasible regions, requiring special global optimization search tools. Conventional optimization methods, such as conjugate gradient, quasi-Newton, and sequential quadratic programming algorithms, which perform brilliantly on a typical local optimization problem, are often trapped into a local minimum and unable to identify the global minimum of the design problem. On the other hand, the computation intensive nature of engineering analysis and simulation software makes the use of many mature stochastic global optimization methods very difficult due to the need of extensive and costly evaluations of the objective and constraint function

An Investigation of Logarithm Decreasing Inertia Weight Particle Swarm Optimization in Global Optimization Problem

ABSTRACT This research investigates Logarithm Decreasing Inertia Weight (LogDIW) to improve the performance of Particle Swarm Optimization (PSO). The general problem of PSO algorithm is premature convergence when solving complex optimization problem. Some researchers try to solve the problem by modifying the PSO or proposing another PSO variants. Some PSO variants proved to have a better performance than the original PSO. The purpose of this research is to obtain some experimental facts to prove the efficiency of LogDIWPSO if the parameters are tuned correctly and to show that the LogDIWPSO performs better compared to the other PSO variants. In the early step of the experiment, a percentage value of search space boundary is obtained. This step is important to compute the velocity threshold of LogDIW based on the optimization problem. The next experiment is done to measure the performance of LogDIWPSO using six benchmark functions in optimization problems and to prove the superiority of LogDIWPSO compared to the other PSO variants. The experiment result shows that LogDIW achieves better performance than the other PSO variants

A unified metaheuristic and system-theoretic framework for petroleum reservoir management

With phenomenal rise in world population as well as robust economic growth in China, India and other emerging economies; the global demand for energy continues to grow in monumental proportions. Owing to its wide end-use capabilities, petroleum is without doubt, the world’s number one energy resource. The present demand for oil and credible future forecasts – which point to the fact that the demand is expected to increase in the coming decades – make it imperative that the E&P industry must device means to improve the present low recovery factor of hydrocarbon reservoirs. Efficiently tailored model-based optimization, estimation and control techniques within the ambit of a closed-loop reservoir management framework can play a significant role in achieving this objective. In this thesis, some fundamental reservoir engineering problems such as field development planning, production scheduling and control are formulated into different optimization problems. In this regard, field development optimization identifies the well placements that best maximizes hydrocarbon recovery, while production optimization identifies reservoir well-settings that maximizes total oil recovery or asset value, and finally, the implementation of a predictive controller algorithm which computes corrected well controls that minimizes the difference between actual outputs and simulated (or optimal) reference trajectory. We employ either deterministic or metaheuristic optimization algorithms, such that the choice of algorithm is purely based on the peculiarity of the underlying optimization problem. Altogether, we present a unified metaheuristic and system-theoretic framework for petroleum reservoir management. The proposed framework is essentially a closed-loop reservoir management approach with four key elements, namely: a new metaheuristic technique for field development optimization, a gradient-based adjoint formulation for well rates control, an effective predictive control strategy for tracking the gradient-based optimal production trajectory and an efficient model-updating (or history matching) – where well production data are used to systematically recalibrate reservoir model parameters in order to minimize the mismatch between actual and simulated measurements. Central to all of these problems is the use of white-box reservoir models which are employed in the well placement optimization and production settings optimization. However, a simple data-driven black-box model which results from the linearization of an identified nonlinear model is employed in the predictive controller algorithm. The benefits and efficiency of the approach in our work is demonstrated through the maximization of the NPV of waterflooded reservoir models that are subject to production and geological uncertainty. Our procedure provides an improvement in the NPV, and importantly, the predictive control algorithm ensures that this improved NPV are attainable as nearly as possible in practice

Project schedule optimisation utilising genetic algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class

Project schedule optimisation utilising genetic algorithms

This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class.EThOS - Electronic Theses Online ServiceGBUnited Kingdo