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

Analysis and modeling of control tasks in dynamic systems

Copyright © 2002 IEEEMost applications of evolutionary algorithms deal with static optimization problems. However, in recent years, there has been a growing interest in time-varying (dynamic) problems, which are typically found in real-world scenarios. One major challenge in this field is the design of realistic test-case generators (TCGs), which requires a systematic analysis of dynamic optimization tasks. So far, only a few TCGs have been suggested. Our investigation leads to the conclusion that these TCGs are not capable of generating realistic dynamic benchmark tests. The result of our research is the design of a new TCG capable of producing realistic nonstationary landscapesRasmus K. Ursem, Thiemo Krink, Mikkel T. Jensen, and Zbigniew Michalewic

ZCS redux

Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payoff-based learning classifier system - ZCS. By using simple difference equation models of ZCS, we show that this system is capable of optimal performance subject to appropriate parameter settings. This is demonstrated for both single- and multistep tasks. Optimal performance of ZCS in well-known, multistep maze tasks is then presented to support the findings from the models

An Improved Parallel Multiple-point Algorithm Using a List Approach

Among the techniques used to simulate categorical variables, multiple-point statistics is becoming very popular because it allows the user to provide an explicit conceptual model via a training image. In classic implementations, the multiple-point statistics are inferred from the training image by storing all the observed patterns of a certain size in a tree structure. This type of algorithm has the advantage of being fast to apply, but it presents some critical limitations. In particular, a tree is extremely RAM demanding. For three-dimensional problems with numerous facies, large templates cannot be used. Complex structures are then difficult to simulate. In this paper, we propose to replace the tree by a list. This structure requires much less RAM. It has three main advantages. First, it allows for the use of larger templates. Second, the list structure being parsimonious, it can be extended to include additional information. Here, we show how this can be used to develop a new approach for dealing with non-stationary training images. Finally, an interesting aspect of the list is that it allows one to parallelize the part of the algorithm in which the conditional probability density function is computed. This is especially important for large problems that can be solved on clusters of PCs with distributed memory or on multicore machines with shared memor

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

Squeak and Rattle Prediction for Robust Product Development

Squeak and rattle are nonstationary, irregular and impulsive sounds that happen inside the car cabin. For decades, customer complaints about squeak and rattle have been, and still are, among the top quality issues in the automotive industry. These annoying sounds are perceived as quality defect indications and burden warranty costs to the car manufacturers. Today, the quality improvements regarding the persistent type of sounds in the car, as well as the increasing popularity of electric engines, as green and quiet propulsion solutions, stress the necessity for suppressing annoying sounds like squeak and rattle more than in the past. The technical solution to this problem is to approach it in the pre-design-freeze phases of the product development and by employing design-concept-related practises. To nail this goal, prediction and evaluation tools and methods are needed to deal with the squeak and rattle quality issues upfront in the product development process. The available tools and methods for prediction of squeak and rattle sounds in the pre-design-freeze phase in a new car development process are not yet sufficiently mature. The existing knowledge gap about the mechanisms behind the squeak and rattle sounds, the lack of accurate simulation and post-processing methods, as well as the computational cost of complex simulations are some of the significant hurdles in this immaturity. This research addresses this problem by identifying a framework for prediction of squeak and rattle sounds in the form of a cause and effect diagram. The main domains and the elements and the sub-contributors to the problem in each domain within this framework are determined through literature studies, field explorations and the conducted descriptive studies on the subject. Further, improvement suggestions for the squeak and rattle evaluation and prediction methods are proposed through prescriptive studies. The applications of some of the proposed methods in the automotive industry are shown and examined in industrial problems

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed