Sameer Alam

Multi-Objective Optimization in Computational Intelligence: Theory and Practice

Multi-Objective Optimization in Computational Intelligence: Theory and Practice explores the theoretical, as well as empirical, performance of MOs on a wide range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. This book provides scholars, academics, and practitioners with a fundamental, comprehensive collection of research on multi-objective optimization techniques, applications, and practices.

ATOMS: Air Traffic Operations and Management Simulator

In this paper, we introduce the air traffic operations and management simulator (ATOMS), which is an air traffic and airspace modeling and simulation system for the analysis of free-flight concepts. This paper describes the design, architecture, functionality, and applications of the ATOMS. It is an intent-based simulator that discretizes the airspace in equal-sized hyper-rectangular cells to maintain intent reference points. It can simulate end-to-end airspace operations and air navigation procedures for conventional air traffic, as well as for free flight. Atmospheric and wind data that are modeled in the ATOMS result in accurate trajectory predictions. The ATOMS uses a multiagent-based modeling paradigm for modular design and easy integration of various air traffic subsystems. A variety of advanced air traffic management (ATM) concepts that are envisioned in free flight are prototyped in the ATOMS, including airborne separation assurance (ASA), cockpit display of traffic information (CDTI), weather avoidance, and decision support systems (DSSs). Experimental results indicate that advanced ATM concepts make a sound case for free flight; however, there is a need to investigate and understand their complex interaction under nonnominal scenarios.

A multi-objective evolutionary method for Dynamic Airspace Re-sectorization using sectors clipping and similarities

Dynamic Airspace Sectorization (DAS) is a future concept in Air Traffic Management. Its main goal is to increase airspace capacity by reshaping - thus optimizing - airspace sector boundaries based on the specifics of different air traffic situations, weather conditions and other factors. The primary objective for the optimization is to balance and reduce the workload of Air Traffic Controllers (ATCs). Many researchers have made efforts in this topic in the past years. However, air traffic changes continually, and DAS has to be adaptive to each change; be it in terms of aircraft density, dynamic routes, fleet mix, etc. Therefore, instead of sectorizing the airspace each time a change occurs, we should re-sectorize it by maintaining maximum similarities between each sectorization. In this paper, we propose a multi-objective evolutionary computation methodology to re-sectorize an airspace. We use a similarity measure between the existing sectorization and the re-sectorization as an objective to maximize during the evolution.We test the methodology with different air traffic conditions with four objective functions: minimize ATC task load standard deviation, maximize average flight sector time, maximize the minimum distance between traffic crossing points and sector boundaries, and maximize the similarity of two airspace sectorizations. Experimental results show that our re-sectorization method is able to perform airspace re-sectorization under different changes in the air traffic, while satisfying the predefined objectives.

MOCCA-II: A multi-objective co-operative co-evolutionary algorithm

Most real-world problems naturally involve multiple conflicting objectives, such as in the case of attempting to maximize both efficiency and safety of a working environment. The aim of multi-objective optimization algorithms is to find those solutions that optimize several components of a vector of objective functions simultaneously. However, when objectives conflict with each other, the multi-objective problem does not have a single optimal solution for all objectives simultaneously. Instead, algorithms attempt to search for the set of efficient solutions, known as the global non-dominated set, that provides solutions that optimally trade-off the objectives. The final solution to be adopted from this set would depend on the preferences of the decision-makers involved in the process. Hence, a decision-maker is typically interested in knowing as many potential solutions as possible. In this paper, we propose an extension to a previous piece of work on multi-objective cooperative coevolution algorithms (MOCCA). The idea was motivated with a practical problem in air traffic management to design terminal airspaces. MOCCA and a further study that was done on a distributed environment for MOCCA, were found to fit the need for the application. We systematically questioned key components of this algorithm and investigated variations to identify a better design. This paper summarizes this systematic investigation and present the resultant new algorithm: multi-objective co-operative co-evolutionary algorithm II (MOCCA-II).

What can make an airspace unsafe? characterizing collision risk using multi-objective optimization

With the continued growth in Air Traffic, researchers are investigating innovative ways to increase airspace capacity while maintaining safety. A key safety indicator for an airspace is its Collision Risk estimate, which is compared against a Target Level of Safety (TLS) to provide a quantitative basis for judging the safety of operations in an airspace. However this quantitative value does not give an insight into the overall collision risk picture for an airspace, and how the risk changes given the interaction of a multitude of factors such as sector/traffic characteristics and controllers actions for flow management. In this paper, we propose an evolutionary framework with multi-objective optimization to evolve collision risk of air traffic scenarios. We attempt to identify, through evolutionary mechanism, the flight events resulting from Air Traffic Controllers actions that can lead to higher collision risks, thereby identifying the contributing factors or the events leading to collision risk. Computational experiments were conducted in an hi-fidelity air traffic simulation environment with collision risk model. Results indicate that “risk-free” traffic scenarios having collision risk below TLS can become “risk-prone” by few flight events, with Climb and Turn maneuvers, specifically during entering and exiting a sector, contributing significantly to increased collision risk.

Pareto meta-heuristics for generating safe flight trajectories under weather hazards

This paper compares ant colony optimization (ACO) and evolutionary multi-objective optimization (EMO) for the weather avoidance in a free flight environment. The problem involves a number of potentially conflicting objectives such as minimizing deviations, weather avoidance, minimizing distance traveled and hard constraints like aircraft performance. Therefore, we modeled the problem as a multi-objective problem with the aim of finding a set of non dominated solutions. This approach is expected to provide pilots the additional degree of freedom necessary for self optimized route planning in Free Flight. Experiments were conducted on a high fidelity air traffic simulator and results indicate that the ACO approach is better suited for this problem, due to its ability to generate solutions in early iterations as well as building better quality non dominated solutions over time.

Multi-Aircraft Dynamic Continuous Descent Approach Methodology for Low-Noise and Emission Guidance

Continuous descent approaches can significantly reduce fuel burn andnoise impact by keeping arriving aircraft at their cruise altitude for longer and then having a continuous descent at near-idle thrust with no level-flight segments. The continuous descent approach procedures arefixed routes that are vertically optimized.With the changing traffic conditions and variable noise-abatement rules, the benefits of continuous descent approach are not yet fully realized. In this paper, a methodology is proposed to generate aircraft-specific dynamic continuous descent approach routes that are both laterally and vertically optimized for noise, emission, and fuel. The methodology involves discretizing the terminal airspace into concentric cylinders with artificial waypoints and uses enumeration and elimination (based on aircraft performance envelope) from one waypoint to another to identify all the possible routes. From the resulting set of possible continuous descent approach routes, routes are identified that represent the best tradeoff on the given objectives. The dynamic continuous descent approach algorithm is implemented in an air traffic simulator for the Sydney, Australia, terminal area. Dynamic continuous descent approach, as compared with a typical continuous descent approach, shows a 14.96% reduction in noise, 11.6% reduction in NO x emission, and 1.5% reduction in fuel burn. The throughput capacity of transition airspace is also investigated for multiple flights performing continuous descent approach operation for different traffic distributions. Themethodology incorporates a delay algorithm that uses the flight’s estimated time of arrival at the intermediate approach fix, which allocates a conflict-free continuous descent approach route by searching through possible routes.

Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms

Usually, Evolutionary Computation (EC) is used for optimisation and machine learning tasks. Recently, a novel use of EC has been proposed – Multiobjective Evolutionary Based Risk Assessment (MEBRA). MEBRA characterises the problem space associated with good and inferior performance of computational algorithms. Problem instances are represented (“scenario Representation”) and evolved (“scenario Generation”) in order to evaluate algorithms (“scenario Evaluation”). The objective functions aim at maximising or minimising the success rate of an algorithm. In the “scenario Mining” step, MEBRA identifies the patterns common in problem instances when an algorithm performs best in order to understand when to use it, and in instances when it performs worst in order to understand when not to use it.

Mapping lessons from ants to free flight: an ant-based weather avoidance algorithm in free flight airspace

The continuing growth of air traffic worldwide motivates the need for new approaches to air traffic management that are more flexible both in terms of traffic volume and weather. Free Flight is one such approach seriously considered by the aviation community. However the benefits of Free Flight are severely curtailed in the convective weather season when weather is highly active, leading aircrafts to deviate from their optimal trajectories. This paper investigates the use of ant colony optimization in generating optimal weather avoidance trajectories in Free Flight airspace. The problem is motivated by the need to take full advantage of the airspace capacity in a Free Flight environment, while maintaining safe separation between aircrafts and hazardous weather. The experiments described herein were run on a high fidelity Free Flight air traffic simulation system which allows for a variety of constraints on the computed routes and accurate measurement of environments dynamics. This permits us to estimate the desired behavior of an aircraft, including avoidance of changing hazardous weather patterns, turn and curvature constraints, and the horizontal separation standard and required time of arrival at a pre determined point, and to analyze the performance of our algorithm in various weather scenarios. The proposed Ant Colony Optimization based weather avoidance algorithm was able to find optimum weather free routes every time if they exist. In case of highly complex scenarios the algorithm comes out with the route which requires the aircraft to fly through the weather cells with least disturbances. All the solutions generated were within flight parameters and upon integration with the flight management system of the aircraft in a Free Flight air traffic simulator, successfully negotiated the bad weather.

Adversarial Evolution: Phase transition in non-uniform hard satisfiability problems

What makes a combinatorial optimization problem hard? The concept of phase transition was introduced in combinatorial decision problems to explain that not all NP-Complete problems are hard, and that there exists a phase transition from solvable to unsolvable problems, within which hard problems exist. Phase transition has been studied using randomly generated problems in which variables have uniform distributions across the different constraints. Real-world problems demonstrate different distributions, however. This paper reveals the relationship between the difficulty of a 3-SAT problem and graph properties. It establishes for the first time a link between the theory of phase-transition in 3-SAT and phase transitions in complex systems and networks. This paper also addresses the question of whether the phase transition phenomenon exists for non-uniform randomly generated problems. A positive answer to this question means in principle that (1) we can generate test problems for combinatorial optimization that are not uniform; (2) we can generate test problems that resemble hard versions of real-world problems; (3) we can identify the features that we need to look for in a problem to test whether or not it is hard. We use a method that we call Adversarial Evolution (AE). In AE, an evolutionary computation method is used to generate hard problem instances by evolving solutions towards the failure of an algorithm and the phase transition region.