Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

The U.S. commercial space launch industry has changed considerably since the enactment of the Commercial Space Launch Amendments Act of 2004. FAA is required to license or permit commercial space launches, but to allow the space tourism industry to develop, the act prohibited FAA from regulating crew and spaceflight participant safety before 2012—a moratorium that was later extended but will now expire on September 30, 2015. Since October 2014, there have been three mishaps involving FAA licensed or permitted launches.

美聯邦航空廳(Federal Aviation Administration)의 航空機 製作檢査 制度의 現況

This paper presents an overview of singular perturbations and time scales (SPaTS) in control theory and applications during the period 1984-2001 (the last such overviews were provided by [231, 371]). Due to the limitations on space, this is in way intended to be an exhaustive survey on the topic.

Singular perturbations and time scales in control theory and applications: An overview

Singular Perturbations and Time Scales in Guidance and Control of Aerospace Systems: A Survey

Challenges of future aircraft propulsion: A review of distributed propulsion technology and its potential application for the all electric commercial aircraft

We describe an ongoing research effort pertaining to the development of a surface traffic automation system that will help controllers to better coordinate surface traffic movements related to arrival and departure traffic. More specifically, we describe the concept for a taxi-planning support tool that aims to optimize the routing and scheduling of airport surface traffic in such a way as to deconflict the taxi plans while optimizing delay, total taxi-time, or some other airport efficiency metric. Certain input parameters related to resource demand, such as the expected landing times and the expected pushback times, are rather difficult to predict accurately. Due to uncertainty in the input data driving the taxi-planning process, the taxi-planning tool is designed such that it produces solutions that are robust to uncertainty. The taxi-planning concept presented herein, which is based on mixed-integer linear programming, is designed such that it is able to adapt to perturbations in these input conditions, as well as to account for failure in the actual execution of surface trajectories. The capabilities of the tool are illustrated in a simple hypothetical airport.

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Optimal airport surface traffic planning using mixed-integer linear programming

This paper describes the development of a new tool that offers significant capabilities for the analysis and design of noise abatement procedures at any given airport. The proposed tool combines a noise model, a geographic information system and a dynamic trajectory optimization algorithm. The optimization algorithm essentially modifies routings and flightpaths such as to minimize the noise impact in the residential communities surrounding the airport, while satisfying all imposed operational and safety constraints. Numerical examples, involving departure trajectories from Amsterdam Airport Schiphol, are included to demonstrate the effectiveness and flexibility of the developed tool. Although the results obtained to date are for departure flights only, the employed methodology tool holds out equal promise for application to approach trajectories. In the numerical examples the characteristics of a Boeing 737-300 aircraft are used.

Optimization of Noise Abatement Departure Trajectories

This paper presents a trade-off study that has been conducted using a recently developed tool for the analysis and design of noise abatement procedures around airports. This new tool, NOISHHH, combines a noise model, a geographic information system, and a dynamic trajectory optimization algorithm. NOISHHH features a multi-objective optimization capability that permits exploration of a variety of environmental criteria, including indices that are generic in nature (e.g. noise footprints) as well as site-specific criteria that take into account the population distribution in the areas surrounding the airport. Using this capability a noise performance trade-off was conducted by comparing arrival trajectories that are optimized according to a variety of noise abatement criteria, including both generic and site-specific criteria. The main focus is on providing insight into the sensitivities in the multi-objective noise performance trade-off process. The results are used to illustrate the possibility of synthesizing trajectories that provide a sensible compromise solution among the various noise criteria.

Generic and site-specific criteria in the optimization of noise abatement trajectories

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Optimal Airport Surface Traffic Planning Using Mixed Integer Linear Programming

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been recognized as a promising method for solving multi-objective optimization problems (MOPs), receiving a lot of attention from researchers in recent years. However, its performance in handling MOPs with complicated Pareto fronts (PFs) is still limited, especially for real-world applications whose PFs are often complex featuring, e.g., a long tail or a sharp peak. To deal with this problem, an improved MOEA/D (named iMOEA/D) that mainly focuses on bi-objective optimization problems (BOPs) is therefore proposed in this paper. To demonstrate the capabilities of iMOEA/D, it is applied to design optimization problems of truss structures. In iMOEA/D, the set of the weight vectors defined in MOEA/D is numbered and divided into two subsets: one set with odd-weight vectors and the other with even-weight vectors. Then, a two-phase search strategy based on the MOEA/D framework is proposed to optimize their corresponding populations. Furthermore, in order to enhance the total performance of iMOEA/D, some recent developments for MOEA/D, including an adaptive replacement strategy and a stopping criterion, are also incorporated. The reliability, efficiency and applicability of iMOEA/D are investigated through seven existing benchmark test functions with complex PFs and three optimal design problems of truss structures. The obtained results reveal that iMOEA/D generally outperforms MOEA/D and NSGA-II in both benchmark test functions and real-world applications.

https://repository.tudelft.nl/islandora/object/uuid%3A0ad89c60-937c-4351-8461-1d75f1fc7eb8/datastream/OBJ/download

Hendrikus G. Visser

Papers

Optimal airport surface traffic planning using mixed-integer linear programming

Optimization of Noise Abatement Departure Trajectories

Generic and site-specific criteria in the optimization of noise abatement trajectories

Optimal Airport Surface Traffic Planning Using Mixed Integer Linear Programming

An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization