Adel Younis

Trends, features, and tests of common and recently introduced global optimization methods

Global optimization techniques have been used extensively due to their capability in handling complex engineering problems. In addition to a number of well known global optimization techniques, many new methods have been introduced recently for various optimal design applications. In this work, a number of representative, well known and recently introduced global optimization techniques are closely examined and compared. The historical development, special features and trends on the development of global optimization algorithms are reviewed. Special attention is devoted to the recent developments of multidisciplinary design optimization algorithms based on effective metamodelling techniques. Commonly used benchmark optimization problems are used as test examples to reveal the pros and cons of these global optimization methods. A new meta-model based global optimization search method, introduced and improved recently by the authors, is also included in the tests and comparison.

Metamodelling and search using space exploration and unimodal region elimination for design optimization

Metamodelling based search, space exploration, and region reduction/elimination methods are effective optimization schemes for computation intensive global design optimization problems. In this work a new metamodelling, space exploration and region reduction search algorithm is introduced. This algorithm, namely Space Exploration and Unimodal Region Elimination (SEUMRE), divides the design space into key unimodal regions using design experiment data; identifies the regions that most likely contain the global minimum; fits Kriging models with additional design experiments using Latin Hypercube designs over these regions; identifies their local minima, and then the global optimum. By identifying promising unimodal regions of the objective and reducing searching space, the method can find the global optimum effectively and efficiently, particularly suited for optimization problems that require extensive computation through engineering analyses and simulations. Comparisons with existing space exploration and region elimination/reduction methods using benchmark test problems have been carried out to demonstrate the advantages of the new method. More robust and problem independent metamodelling improvements are under study.

Approximated unimodal region elimination-based global optimisation method for engineering design

Computer analysis and simulation-based design optimisation requires more computationally efficient global optimisation tools. In this work, a new global optimisation algorithm based on design experiments, region elimination and response surface modelling, namely, the Approximated Unimodal Region Elimination (AUMRE) method, is introduced. The approach divides the field of interest into several unimodal regions using design experiment data, identifies and ranks the regions that most likely contain the global minimum, forms a response surface model using additional design experiment data over the most promising region, identifies its minimum, removes this processed region and moves to the next most promising region. By avoiding redundant searches, the approach identifies the global optimum with a reduced number of objective function evaluations and computation effort. The new algorithm was tested using a variety of benchmark global optimisation problems and compared with several widely used global optimisation algorithms. The results present a comparable search accuracy and superior computation efficiency, making the new algorithm an ideal tool for computer analysis and simulation-based global design optimisation.

Approximated Unimodal Region Elimination Based Global Optimization Method for Engineering Design

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.Copyright

Global Optimization Using Mixed Surrogate Models for Computation Intensive Designs

Despite of today’s steady and continuing improvement of computation power, effective use of complex and computational intensive engineering analysis and simulation codes in design optimization remains a challenge. In this work, a new global optimization algorithm, namely Mixed Surrogate Models and Design Space Elimination Search (MSMDSES), is introduced. The approach divides the field of interest into several unimodal regions; identify and rank the regions that likely contain the global minimum; fits a Radial Basis function and Quadratic Response Surface model over each promising region with additional design experiments data points using Latin Hypercube designs; identifies its minimum and removes the processed region; and moves to the next most promising region until all regions are processed and the global optimum is identified. The new algorithm was tested using several benchmark problems for global optimization and compared with several widely used region elimination and space exploration global optim...

Metamodel multi-objective optimization tool for mechatronic system design

The use of approximation models in multiobjective optimization problems that involve expensive analysis and simulation processes such as multi-physics modeling and simulation, finite element analysis (FEA) and computational fluid dynamics (CFD) has become more popular and more attractive, especially for the optimization of complex mechatronics systems. Approximation models have been found as a promising tool for multiobjective optimization problems due to their capability for providing accurate modeling results with much less computations for intensive computation problems. Many present global optimization search techniques involve fitness evaluations that are expensive to perform, even worse for problems with multiple objective black-box functions evaluations. In this work, a new adaptive multiobjective optimization approach based metamodeling (AMOP) techniques is introduced. The approach can identify the Pareto front for multiobjective optimization problems efficiently with high accuracy. The computation cost associated with identifying the Pareto front for expensive black-box functions is reduced. The new search method was tested using benchmark test problems and mechatronics device design examples.

Application of the new SEUMRE global optimisation tool in high efficiency EV/PHEV/EREV electric mode operations

Electric vehicles (EV/HEV/PHEV/EREV) draw mechanical power or regenerate electric power using multiple electric motors and generators (M/Gs). To achieve optimal vehicles electrical/mechanical energy conversion efficiency and to prolong the pure electric range of these vehicles, the energy conversion efficiency is to be maximised against powertrain component operation parameters using high fidelity model and simulation. However, the energy conversion efficiency model using vehicle powertrain component model and simulation is complex and computationally intensive. An efficient global optimisation tool is needed to produce the optimal efficiency look-up surface for real-time control system implementation, or to search for the optimal operation parameters in real time. In this work, a new two-mode-plus EREV design is used. The optimal vehicle energy conversion efficiency under various powertrain component operation parameters are obtained using three alternative global optimisation tools, GA, PSO, and SEUMRE....

Global Optimization Using Mixed Surrogates and Space Elimination in Computationally Intensive Engineering Designs

Surrogate-based modeling is an effective search method for global design optimization over well-defined areas using complex and computationally intensive analysis and simulation tools. However, indentifying the appreciate surrogate models and their suitable areas remains a challenge that requires extensive human intervention. In this work, a new global optimization algorithm, namely Mixed Surrogate and Space Elimination (MSSE) method, is introduced. Representative surrogate models, including Quadratic Response Surface, Radial Basis function, and Kriging, are mixed with different weight ratios to form an adaptive metamodel with best tested performance. The approach divides the field of interest into several unimodal regions; identifies and ranks the regions that likely contain the global minimum; fits the weighted surrogate models over each promising region using additional design experiment data points from Latin Hypercube Designs and adjusts the weights according to the performance of each model; identifies its minimum and removes the processed region; and moves to the next most promising region until all regions are processed and the global optimum is identified. The proposed algorithm was tested using several benchmark problems for global optimization and compared with several widely used space exploration global optimization algorithms, showing reduced computation efforts, robust performance and comparable search accuracy, making the proposed method an excellent tool for computationally intensive global design optimization problems.

Application of New SEUMRE Global Optimization Tool in High Efficiency EV/PHEV/EREV Electric Mode Operations

Electric vehicles (EV/HEV/PHEV/EREV) draw mechanical power or regenerate electric power using multiple electric motors and generators (M/Gs). To achieve optimal vehicles electrical/mechanical energy conversion efficiency and to prolong the pure electric range of these vehicles, the energy conversion efficiency is to be maximised against powertrain component operation parameters using high fidelity model and simulation. However, the energy conversion efficiency model using vehicle powertrain component model and simulation is complex and computationally intensive. An efficient global optimisation tool is needed to produce the optimal efficiency look-up surface for real-time control system implementation, or to search for the optimal operation parameters in real time. In this work, a new two-mode-plus EREV design is used. The optimal vehicle energy conversion efficiency under various powertrain component operation parameters are obtained using three alternative global optimisation tools, GA, PSO, and SEUMRE. Application of SEUMRE allow refined and more accurate vehicle energy conversion efficiency map being created for the optimal operation of the EV/PHEV/EREV. Optimal vehicle control schemes can then be generated in determining the speed and torque of the M/Gs of the vehicle without violating their physical constraints and achieving the overall maximum efficiency of the hybrid powertrain system.

Optimal Operation of a Self-regulating Smart Distribution System with Wind Energy Integration and Demand Response

As an integral part of a smart grid, the smart distribution system is an important concept that employs advanced communication, control and information technologies to manage and optimize the resources of a feeder in order to (1) improve energy efficiency and customer power consumption patterns, (2) increase penetration and storage of Renewable Energy (RE) thereby decreasing GHG emissions, and (3) enable markets, consumer motivation, and participation. For electrical distribution systems and demand-side management, demand response (DR) control is an emerging concept to manage customer power consumption patterns in response to system operation conditions, and to minimize (or provide) system ancillary services while maintaining customer-side comfortable usage requirements. Reliable bidirectional smart grid communications and customer’s grid-friendly participation provide new opportunities that enable DR to be employed to optimize grid operation utilizing the energy storage capability of modern homes via control of heat pumps and, in the near future, plug-in electric vehicles (PEVs). Considering the complex interactions between an electrical distribution network and grid resources, in a quasi-steady-state simulation environment, optimal operation and management requires robust global optimization techniques that also incorporate distribution load flow simulations to optimally integrate RE generation, loads and corresponding DR control strategy. System power loss reduction was selected as an objective for the optimal distribution load flow optimization and the optimization process simulates load calculations and demand-side DR resource control. Residential heat pumps with thermal energy storage were chosen as typical DR resources to help regulate system power balance. An advanced metamodel-based global optimization (MBGO) search tool, recently presented, named space exploration and unimodal region elimination (SEUMRE) algorithm, was applied to determine minimum system power losses and optimal DR resources operation to offset wind fluctuations. This MBGO tool solves complex global design optimization problems with black-box objective/constraint functions and is ideally suited to this complex, computationally intensive application. The optimal control solution was compared with the unoptimized results to show the benefit of the proposed advanced optimizer. The inability to achieve an optimized solution and the poor computational efficiency of conventional optimization approaches in identifying the correct global optimum are also illustrated.