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

A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

Advanced quantum based neural network classifier and its application for objectionable web content filtering

© 2013 IEEE. In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content

Quantum-Inspired Particle Swarm Optimization for Feature Selection and Parameter Optimization in Evolving Spiking Neural Networks for Classification Tasks

Introduction: Particle Swarm Optimization (PSO) was introduced in 1995 by Russell Eberhart and James Kennedy (Eberhart & Kennedy, 1995). PSO is a biologically-inspired technique based around the study of collective behaviour in decentralized and self-organized animal society systems. The systems are typically made up from a population of candidates (particles) interacting with one another within their environment (swarm) to solve a given problem. Because of its efficiency and simplicity, PSO has been successfully applied as an optimizer in many applications such as function optimization, artificial neural network training, fuzzy system control. However, despite recent research and development, there is an opportunity to find the most effective methods for parameter optimization and feature selection tasks. This chapter deals with the problem of feature (variable) and parameter optimization for neural network models, utilising a proposed Quantum–inspired PSO (QiPSO) method. In this method the features of the model are represented probabilistically as a quantum bit (qubit) vector and the model parameter values as real numbers. The principles of quantum superposition and quantum probability are used to accelerate the search for an optimal set of features, that combined through co-evolution with a set of optimised parameter values, will result in a more accurate computational neural network model. The method has been applied to the problem of feature and parameter optimization in Evolving Spiking Neural Network (ESNN) for classification. A swarm of particles is used to find the most accurate classification model for a given classification task. The QiPSO will be integrated within ESNN where features and parameters are simultaneously and more efficiently optimized. A hybrid particle structure is required for the qubit and real number data types. In addition, an improved search strategy has been introduced to find the most relevant and eliminate the irrelevant features on a synthetic dataset. The method is tested on a benchmark classification problem. The proposed method results in the design of faster and more accurate neural network classification models than the ones optimised through the use of standard evolutionary optimization algorithms. This chapter is organized as follows. Section 2 introduces PSO with quantum information principles and an improved feature search strategy used later in the developed method. Section 3 is an overview of ESNN, while Section 4 gives details of the integrated structure and the experimental results. Finally, Section 5 concludes this chapter

Fast Search Approaches for Fractal Image Coding: Review of Contemporary Literature

Fractal Image Compression FIC as a model was conceptualized in the 1989 In furtherance there are numerous models that has been developed in the process Existence of fractals were initially observed and depicted in the Iterated Function System IFS and the IFS solutions were used for encoding images The process of IFS pertaining to any image constitutes much lesser space for recording than the actual image which has led to the development of representation the image using IFS form and how the image compression systems has taken shape It is very important that the time consumed for encoding has to be addressed for achieving optimal compression conditions and predominantly the inputs that are shared in the solutions proposed in the study depict the fact that despite of certain developments that has taken place still there are potential chances of scope for improvement From the review of exhaustive range of models that are depicted in the model it is evident that over period of time numerous advancements have taken place in the FCI model and is adapted at image compression in varied levels This study focus on the existing range of literature on FCI and the insights of various models has been depicted in this stud

Real Coded Mixed Integer Genetic Algorithm for Geometry Optimization of Flight Simulator Mechanism Based on Rotary Stewart Platform

Featured Application Low-cost flight simulators with electric rotary actuators and optimized geometry for flight simulation. Designing the motion platform for the flight simulator is closely coupled with the particular aircraft's flight envelope. While in training, the pilot on the motion platform has to experience the same feeling as in the aircraft. That means that flight simulators need to simulate all flight cases and forces acting upon the pilot during flight. Among many existing mechanisms, parallel mechanisms based on the Stewart platform are suitable because they have six degrees of freedom. In this paper, a real coded mixed integer genetic algorithm (RCMIGA) is applied for geometry optimization of the Stewart platform with rotary actuators (6-RUS) to design a mechanism with appropriate physical limitations of workspace and motion performances. The chosen algorithm proved that it can find the best global solution with all imposed constraints. At the same time, the obtained geometry can be manufactured because integer solutions can be mapped to available discrete values. Geometry is defined with a minimum number of parameters that fully define the mechanism with all constraints. These geometric parameters are then optimized to obtain custom-tailored geometry for aircraft flight simulation

CQICO and multiobjective thermal optimization for high-speed PM generator

This paper proposes a novel Continuous Quantum Immune Clonal Optimization (CQICO) algorithm for thermal optimization on an 117kW high speed permanent magnet generator (HSPMG). The proposed algorithm mixes the Quantum Computation into the Immune Cloning Algorithm and causes better population diversity, higher global searching ability, and faster convergence which approved by simulation results. Then, the improved algorithm is applied to seek an optimized slot groove and improve HSPMG thermal performance, in which the 3-D fluid-thermal coupling analyses are processed with a multi-objective optimal group composed of the highest temperature and the temperature difference. Both the proposed algorithm and the obtained conclusions are of significances in the design and optimization of the cooling system in electric machines

A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles

Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes