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

Optimal distributed generation planning based on NSGA-II and MATPOWER

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe UK and the world are moving away from central energy resource to distributed generation (DG) in order to lower carbon emissions. Renewable energy resources comprise a big percentage of DGs and their optimal integration to the grid is the main attempt of planning/developing projects with in electricity network. Feasibility and thorough conceptual design studies are required in the planning/development process as most of the electricity networks are designed in a few decades ago, not considering the challenges imposed by DGs. As an example, the issue of voltage rise during steady state condition becomes problematic when large amount of dispersed generation is connected to a distribution network. The efficient transfer of power out or toward the network is not currently an efficient solution due to phase angle difference of each network supplied by DGs. Therefore optimisation algorithms have been developed over the last decade in order to do the planning purpose optimally to alleviate the unwanted effects of DGs. Robustness of proposed algorithms in the literature has been only partially addressed due to challenges of power system problems such multi-objective nature of them. In this work, the contribution provides a novel platform for optimum integration of distributed generations in power grid in terms of their site and size. The work provides a modified non-sorting genetic algorithm (NSGA) based on MATPOWER (for power flow calculation) in order to find a fast and reliable solution to optimum planning. The proposed multi-objective planning tool, presents a fast convergence method for the case studies, incorporating the economic and technical aspects of DG planning from the planner‟s perspective. The proposed method is novel in terms of power flow constraints handling and can be applied to other energy planning problems

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

Lost in optimisation of water distribution systems? A literature review of system design

This is the final version of the article. Available from MDPI via the DOI in this record.Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details

Adaptive Search and Constraint Optimisation in Engineering Design

The dissertation presents the investigation and development of novel adaptive computational techniques that provide a high level of performance when searching complex high-dimensional design spaces characterised by heavy non-linear constraint requirements. The objective is to develop a set of adaptive search engines that will allow the successful negotiation of such spaces to provide the design engineer with feasible high performance solutions. Constraint optimisation currently presents a major problem to the engineering designer and many attempts to utilise adaptive search techniques whilst overcoming these problems are in evidence. The most widely used method (which is also the most general) is to incorporate the constraints in the objective function and then use methods for unconstrained search. The engineer must develop and adjust an appropriate penalty function. There is no general solution to this problem neither in classical numerical optimisation nor in evolutionary computation. Some recent theoretical evidence suggests that the problem can only be solved by incorporating a priori knowledge into the search engine. Therefore, it becomes obvious that there is a need to classify constrained optimisation problems according to the degree of available or utilised knowledge and to develop search techniques applicable at each stage. The contribution of this thesis is to provide such a view of constrained optimisation, starting from problems that handle the constraints on the representation level, going through problems that have explicitly defined constraints (i.e., an easily computed closed form like a solvable equation), and ending with heavily constrained problems with implicitly defined constraints (incorporated into a single simulation model). At each stage we develop applicable adaptive search techniques that optimally exploit the degree of available a priori knowledge thus providing excellent quality of results and high performance. The proposed techniques are tested using both well known test beds and real world engineering design problems provided by industry.British Aerospace, Rolls Royce and Associate

Emerging trends in optimal structural health monitoring system design: From sensor placement to system evaluation

This paper presents a review of advances in the field of Sensor Placement Optimisation (SPO) strategies for Structural Health Monitoring (SHM). This task has received a great deal of attention in the research literature, from initial foundations in the control engineering literature to adoption in a modal or system identification context in the structural dynamics community. Recent years have seen an increasing focus on methods that are specific to damage identification, with the maximisation of correct classification outcomes being prioritised. The objectives of this article are to present the SPO for SHM problem, to provide an overview of the current state of the art in this area, and to identify promising emergent trends within the literature. The key conclusions drawn are that there remains a great deal of scope for research in a number of key areas, including the development of methods that promote robustness to modelling uncertainty, benign effects within measured data, and failures within the sensor network. There also remains a paucity of studies that demonstrate practical, experimental evaluation of developed SHM system designs. Finally, it is argued that the pursuit of novel or highly efficient optimisation methods may be considered to be of secondary importance in an SPO context, given that the optimisation effort is expended at the design stage