Multi-dwelling refurbishment optimization: problem decomposition, solution and trade-off analysis

A methodology has been developed for the multiobjective optimization of the refurbishment of domestic building stock on a regional scale. The approach is based on the decomposition of the problem into two stages: first to find the energy-cost trade-off for individual houses, and then to apply it tomultiple houses. The approach has been applied to 759 dwellings using buildings data from a survey of the UK housing stock. The energy use of each building and their refurbished variants were simulated using EnergyPlus using automatically-generated input files. The variation in the contributing refurbishment options from least to highest cost along the Pareto front shows loft and cavity wall insulation to be optimal intially, and solid wall insulation and double glazing appearing later

Multi-dwelling Refurbishment Optimization: Problem Decomposition, Solution, and Trade-off Analysis

A methodology has been developed for the multiobjective optimization of the refurbishment of domestic building stock on a regional scale. The approach is based on the decomposition of the problem into two stages: first to find the energy-cost trade-off for individual houses, and then to apply it tomultiple houses. The approach has been applied to 759 dwellings using buildings data from a survey of the UK housing stock. The energy use of each building and their refurbished variants were simulated using EnergyPlus using automatically-generated input files. The variation in the contributing refurbishment options from least to highest cost along the Pareto front shows loft and cavity wall insulation to be optimal intially, and solid wall insulation and double glazing appearing later

Robust multi-objective design of suspension systems

This thesis presents a robust multi-objective optimal design of four-degree-of-freedom passive and semi-active suspension systems. The passive suspension system is used in a racing car and the semi-active suspension is implemented on a passenger car. Mathematical models of the commercial and racing vehicle suspension systems are used in the computer simulations. A robust multi-objective design of the suspension systems is carried out by considering the minimization of three objectives: passenger’s head acceleration (HA), suspension deflection (SD), and tire deflection (TD). The first objective is concerned with the passenger’s health and comfort. The suspension stroke is described by SD and the tire holding is characterized by TD. The optimal design of the passive suspension involves tuning the coefficients of the sprung spring and damper, tire stiffness, and inertance of the inerter. Suspension systems’ parametric variations are very common and cannot be avoided in practice. To this end, a robust multi-objective optimization method that takes into consideration small changes in the design parameters should be considered. Unlike traditional multi-objective optimization problems where the focus is placed on finding the global Pareto-optimal solutions which express the optimal trade-offs among design objectives, the robust multi-objective optimization algorithms are concerned with robust solutions that are less sensitive to perturbations of decision variables. As a result, the mean effective values of the fitness functions are used as design objectives. Constraints on the design parameters and goals are applied. Numerical simulations show that the robust multi-objective design (RMOD) is very effective and guarantees a robust behavior as compared to that of the classical multi-objective design (MOD). The results also show that the robust region is inside the feasible search space and avoids all of its boundaries. The decision parameter space of the semi-active suspension includes both passive and active components. The passive components include the stiffness of the sprung spring, damping coefficient of the shock absorber, and stiffness of the tire. The active elements are the design details of the LQR algorithm. During the design, global sensitivity analysis is conducted to determine the elements of the suspension system that have high impact on the design objectives. The mass of the passenger’s head and upper body, the mass of the passenger’s lower body and cushion, passenger and cushion’s elastic properties, and the sprung mass of the vehicle are selected for the sensitivity analysis. Results show that the design goals are more sensitive to the variations in the sprung mass than the other parameters. As a result, parametric variations in the sprung mass of the vehicle and passive elements of the suspension system are considered. Similar to the design of the passive suspension, the mean effective values of SD, TD, and HA are used as design objectives. Also, constraints are applied on the objectives in compliance with the requirements of ISO 2631-1 on the design of car suspension systems. The optimization problem is solved by the NSGA-II (non-dominated sorting genetic algorithm) and robust Pareto front and set are obtained

Multi-objective Optimization of Multi-loop Control Systems

Cascade Control systems are composed of inner and outer control loops. Compared to the traditional single feedback controls, the structure of cascade controls is more complex. As a result, the implementation of these control methods is costly because extra sensors are needed to measure the inner process states. On the other side, cascade control algorithms can significantly improve the controlled system performance if they are designed properly. For instance, cascade control strategies can act faster than single feedback methods to prevent undesired disturbances, which can drive the controlled system’s output away from its target value, from spreading through the process. As a result, cascade control techniques have received much attention recently. In this thesis, we present a multi-objective optimal design of linear cascade control systems using a multi-objective algorithm called the non-dominated sorting genetic algorithm (NSGA-II), which is one of the widely used algorithms in solving multi-objective optimization problems (MOPs). Two case studies have been considered. In the first case, a multi-objective optimal design of a cascade control system for an underactuated mechanical system consisting of a rotary servo motor, and a ball and beam is introduced. The setup parameters of the inner and outer control loops are tuned by the NSGA-II to achieve four objectives: 1) the closed-loop system should be robust against inevitable internal and outer disturbances, 2) the controlled system is insensitive to inescapable measurement noise affecting the feedback sensors, 3) the control signal driving the mechanical system is optimum, and 4) the dynamics of the inner closed-loop system has to be faster than that of the outer feedback system. By using the NSGAII algorithm, four design parameters and four conflicting objective functions are obtained. The second case study investigates a multi-objective optimal design of an aeroelastic cascade controller applied to an aircraft wing with a leading and trailing control surface. The dynamics of the actuators driving the control surfaces are considered in the design. Similarly, the NSGA-II is used to optimally adjust the parameters of the control algorithm. Ten design parameters and three conflicting objectives are considered in the design: the controlled system’s tracking error to an external gust load should be minimal, the actuators should be driven by minimum energy, and the dynamics of the closed-loop comprising the actuators and inner control algorithm should be faster than that of the aeroelastic structure and the outer control loop. Computer simulations show that the presented case studies may become the basis for multi-objective optimal design of multi-loop control systems

Multidisciplinary and Multi-Objective Optimal Design of a Cascade Control System for a Flexible Wing with Embedded Control Surfaces Having Actuator Dynamics

A multidisciplinary and multi-objective optimization approach that integrates the design of the control surfaces’ sizes, active control systems, and estimator for an aircraft’s wing with three control surfaces is developed. Due to its attractive stability robustness properties, a control system based on the LQR (Linear Quadratic Regulator) is built for each control surface. The geometrical parameters of the control surfaces such as the span wise and chord lengths, the design details of the LQR penalty matrices, and the locations of the estimator poles are tuned by a widely used multi-objective optimization algorithm called NSGA-II (Non-dominated Sorting Genetic Algorithm). Four objectives are considered: minimizing impacts of external gust loads, maximizing stability robustness and extending flutter boundaries, reducing control energy consumption, and minimizing the Frobenius norm of the estimator gains. The solution of the multi-objective optimization problem is a set called Pareto set and the set of the corresponding function evaluation is called Pareto front. The solution set contains various geometrical configurations of the control surfaces with different feedback gains, which represent different degrees of optimal compromises among the design objectives. The optimization results demonstrate the competing relationship between the design objectives and necessity of handling the design problem in a multidisciplinary and multi-objective context. Three major results are obtained from inspecting the profiles of the closed-loop eigenvalues at various airspeeds 1) a unique control gain can be designed for the entire flight envelope, 2) the flutter boundaries can be infinitely extended, and 3) a unique observer gain can be designed for the entire flight envelope. The third chapter of this thesis presents a multi-objective and multidisciplinary optimal design of a cascade control system for an aircraft wing with four aerodynamic ailerons actuated by four identical brushless DC motors. The design of the control system is broken into a secondary and primary control algorithm. The primary control algorithm is designed based on the concept of LQR and then applied to mathematical model of the wing and its control surfaces to calculate their required deflections. The output of the primary controller serves as set-point for the secondary control loop which consists of the dynamic of the DC motor and Proportional Velocity (PV) based controller. Then, an optimal design of the control algorithms is carried out in multi-objective and multidisciplinary settings. Three objectives are considered: 1) the speed of response of the secondary controlled system must be faster than that of the primary one, 2) the controlled system must be robust against external disturbances affecting both control layers, and 3) optimal energy consumption. The decision variables of the primary as well as secondary control algorithms and the sizing elements of the control surfaces form the design parameter space of the optimization problem. Both geometrical and dynamic constraints are applied on the setup parameters. The multi-objective optimization problem (MOP) is solved by NSGA-II, which is one of the popular algorithms in solving MOPs. The solution of the MOP is a set of optimal control algorithms that represent the conflicts among the design objectives. Numerical simulations show that the design goals are achieved, the secondary control is always fast enough to prevent the propagation of disturbances to the primary loop, the inner and outer control algorithms are robust against disturbance inputs, and the primary control loop stays stable when the air stream velocity varies from 80 to 1000 (⁄) even at its worst relative stability value. The presented study may become the basis for multi-objective and multidisciplinary optimal design for aeroelastic structure having actuator dynamics

A review of population-based metaheuristics for large-scale black-box global optimization: Part B

This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research