Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Peer reviewedPostprin

Multiobjective Engineering Design Optimization Problems: A Sensitivity Analysis Approach

International audienceThis paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternatives are preferred over others during the multiobjective optimization process. While taking the first approach, the designer chooses the design variable and/or parameter that causes uncertainties. The designer then associates a robustness index with each design alternative and adds each index as an objective function in the optimization problem. For the second approach, the designer must know, a priori, the interval of variation in the design variables or in the design environment parameters, because the designer will be accepting the interval of variation in the objective functions. The second method does not require any law of probability distribution of uncontrollable variations. Finally, the authors give two illustrative examples to highlight the contributions of the paper

Robust design of a passive wind turbine system

The effectiveness of full passive Wind Turbine (WT) systems has been recently demonstrated. Such low cost and reliable structures without active control and with a minimum number of sensors can be efficient only if the system design parameters are mutually adapted through an integrated optimal design approach. Even if there is a good agreement between theoretical design models and an experimental prototype, it is relevant to evaluate the WT efficiency with respect to design variable variations. Thus, this work is devoted more specifically to the sensitivity analysis of a passive WT system according to electrical variable variations of the Permanent Magnet Synchronous Generator (PMSG). It also investigates the interest of a robust design approach for reducing the sensitivity of the WT efficiency to specific variable variations

Evidence-based robust design of deflection actions for near Earth objects

This paper presents a novel approach to the robust design of deflection actions for Near Earth Objects (NEO). In particular, the case of deflection by means of Solar-pumped Laser ablation is studied here in detail. The basic idea behind Laser ablation is that of inducing a sublimation of the NEO surface, which produces a low thrust thereby slowly deviating the asteroid from its initial Earth threatening trajectory. This work investigates the integrated design of the Space-based Laser system and the deflection action generated by laser ablation under uncertainty. The integrated design is formulated as a multi-objective optimisation problem in which the deviation is maximised and the total system mass is minimised. Both the model for the estimation of the thrust produced by surface laser ablation and the spacecraft system model are assumed to be affected by epistemic uncertainties (partial or complete lack of knowledge). Evidence Theory is used to quantify these uncertainties and introduce them in the optimisation process. The propagation of the trajectory of the NEO under the laser-ablation action is performed with a novel approach based on an approximated analytical solution of Gauss’ Variational Equations. An example of design of the deflection of asteroid Apophis with a swarm of spacecraft is presented

APPROXIMATION ASSISTED MULTIOBJECTIVE AND COLLABORATIVE ROBUST OPTIMIZATION UNDER INTERVAL UNCERTAINTY

Optimization of engineering systems under uncertainty often involves problems that have multiple objectives, constraints and subsystems. The main goal in these problems is to obtain solutions that are optimum and relatively insensitive to uncertainty. Such solutions are called robust optimum solutions. Two classes of such problems are considered in this dissertation. The first class involves Multi-Objective Robust Optimization (MORO) problems under interval uncertainty. In this class, an entire system optimization problem, which has multiple nonlinear objectives and constraints, is solved by a multiobjective optimizer at one level while robustness of trial alternatives generated by the optimizer is evaluated at the other level. This bi-level (or nested) MORO approach can become computationally prohibitive as the size of the problem grows. To address this difficulty, a new and improved MORO approach under interval uncertainty is developed. Unlike the previously reported bi-level MORO methods, the improved MORO performs robustness evaluation only for optimum solutions and uses this information to iteratively shrink the feasible domain and find the location of robust optimum solutions. Compared to the previous bi-level approach, the improved MORO significantly reduces the number of function calls needed to arrive at the solutions. To further improve the computational cost, the improved MORO is combined with an online approximation approach. This new approach is called Approximation-Assisted MORO or AA-MORO. The second class involves Multiobjective collaborative Robust Optimization (McRO) problems. In this class, an entire system optimization problem is decomposed hierarchically along user-defined domain specific boundaries into system optimization problem and several subsystem optimization subproblems. The dissertation presents a new Approximation-Assisted McRO (AA-McRO) approach under interval uncertainty. AA-McRO uses a single-objective optimization problem to coordinate all system and subsystem optimization problems in a Collaborative Optimization (CO) framework. The approach converts the consistency constraints of CO into penalty terms which are integrated into the subsystem objective functions. In this way, AA-McRO is able to explore the design space and obtain optimum design solutions more efficiently compared to a previously reported McRO. Both AA-MORO and AA-McRO approaches are demonstrated with a variety of numerical and engineering optimization examples. It is found that the solutions from both approaches compare well with the previously reported approaches but require a significantly less computational cost. Finally, the AA-MORO has been used in the development of a decision support system for a refinery case study in order to facilitate the integration of engineering and business decisions using an agent-based approach

Control of robust design in multiobjective optimization under uncertainties

In design and optimization problems, a solution is called robust if it is stable enough with respect to perturbation of model input parameters. In engineering design optimization, the designer may prefer a use of robust solution to a more optimal one to set a stable system design. Although in literature there is a handful of methods for obtaining such solutions, they do not provide a designer with a direct and systematic control over a required robustness. In this paper, a new approach to robust design in multiobjective optimization is introduced, which is able to generate robust design with model uncertainties. In addition, it introduces an opportunity to control the extent of robustness by designer preferences. The presented method is different from its other counterparts. For keeping robust design feasible, it does not change any constraint. Conversely, only a special tunable objective function is constructed to incorporate the preferences of the designer related to the robustness. The effectiveness of the method is tested on well known engineering design problems