Resilient Coverage: Exploring the Local-to-Global Tradeoff Ragesh K. Ramachandran 1, Lifeng Zhou 2 and Gaurav S. Sukhatme 1 Abstract -- We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, the framework repositions robots in a user-defined local neighborhood of the failed robot to compensate for the coverage loss. The central controller augments the team with additional robots from the robot pool when simply repositioning robots fails to attain a user-specified level of desired coverage. The size of the local neighborhood around the failed robot and the desired coverage over the region are two settings that can be manipulated to achieve a user-specified balance. We investigate the tradeoff between the coverage compensation achieved through local repositioning and the computation required to plan the new robot locations. We also study the relationship between the size of the local neighborhood and the number of additional robots added to the team for a given user-specified level of desired coverage. The computational complexity of our resilient strategy (tunable resilient coordination), is quadratic in both neighborhood size and number of robots in the team. At first glance, it seems that any desired level of coverage can be efficiently achieved by augmenting the robot team with more robots while keeping the neighborhood size fixed. However, we show that to reach a high level of coverage in a neighborhood with a large robot population, it is more efficient to enlarge the neighborhood size, instead of adding additional robots and repositioning them.

We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation. The input to our method is a dataset (or ensemble of datasets) corresponding to a particular solution (or ensemble of particular solutions) of a differential equation. The output is a human-readable differential equation with parameters calibrated to the individual particular solutions provided. The key to our method is to learn differentiable models of the data that subsequently serve as inputs to a genetic programming algorithm in which graphs specify computation over arbitrary compositions of functions, parameters, and (potentially differential) operators on functions. Differential operators are composed and evaluated using recursive application of automatic differentiation, allowing our algorithm to explore arbitrary compositions of operators without the need for human intervention. We also demonstrate an active learning process to identify and remedy deficiencies in the proposed governing equations.

Over the last three decades more then sixty meta-heuristic algorithms have been proposed by the various authors. Such algorithms are inspired from physical phenomena, animal behavior or evolutionary concepts. These algorithms have been widely used for solving the various real world optimization problems. Researchers are continuously working to improve the existing algorithms and also proposing new algorithms that are giving competitive results as compared to the existing algorithms present in the literature. In this paper a novel meta heuristic algorithm based on military dogs squad is introduced. The proposed algorithm mimics the searching capability of the trained military dogs. Military dogs have strong smell senses by which they are able to search the suspicious objects like bombs, wildlife scats, currency, or blood as well as they can communicate with each other by their barking. The performance of the proposed algorithm is tested on 17 benchmark functions and compared with five other meta-heuristics namely particle swarm optimization (PSO), multiverse optimizer (MVO), genetic algorithm (GA), probability based learning (PBIL) and evolutionary strategy (ES). The results are validated in terms of mean and standard deviation of the fitness value. The convergence behavior and consistency of the results have been also validated by plotting convergence graphs and BoxPlots. Further the, proposed algorithm is successfully utilized to solve the real world fake review detection problem. The experimental results demonstrate that the proposed algorithm outperforms the other considered algorithms on the majority of performance parameters.

This paper proposes a formulation of the Active Debris Removal (ADR) Mission Design problem as a modified Time-Dependent Traveling Salesman Problem (TDTSP). The TDTSP is a well-known combinatorial optimization problem, whose solution is the cheapest mono-cyclic tour connecting a number of non-stationary cities in a map. The problem is tackled with an optimization procedure based on Simulated Annealing, that efficiently exploits a natural encoding and a careful choice of mutation operators. The developed algorithm is used to simultaneously optimize the targets sequence and the rendezvous epochs of an impulsive ADR mission. Numerical results are presented for sets comprising up to 20 targets. INTRODUCTION The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem, whose solution is the cheapest tour which allows a salesman to visit, only once, a number of cities in a map; the cost of each city-to-city transfer is, typically, the traveled distance or the fuel consumption. Active Debris Removal (ADR) missions can be seen as peculiar instances of the TDTSP, where an active (chaser) spacecraft is asked to visit, that is, to perform a rendezvous, with a certain number of targets (space debris), making the best use of the on-board propellant. Such kind of missions are increasing in popularity among space agencies all over the world, as the sustainability of the extra-atmospheric environment is becoming compromised by the huge amount of "space garbage" now orbiting Earth. A cost-competitive space program would involve the removal of several dozens of small debris with each single mission; such a complex scenario could became feasible only with the best possible use of the propellant on-board of the chaser spacecraft. As a consequence, a well-designed ADR mission would require the optimization of a multi-target rendezvous trajectory. A number of authors dealt with long term or time-free ADR missions aimed at removing a small number of debris from Sun synchronous orbits (at a rate of three to ten per year). These missions heavily rely on J 2 orbital perturbation for the alignment of the orbital planes of consecutive targets before starting the rendezvous maneuver, in order to reduce the mission cost.

Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their successful applications in fields including engineering and health sciences. In this work, we investigate the use of a deep learning (DL) model as an alternative tool to do so. The proposed method, called MaNet, is motivated by the fact that most of the DL models often need to solve massive nasty optimization problems consisting of millions of parameters. Feature selection is the main adopted concepts in MaNet that helps the algorithm to skip irrelevant or partially relevant evolutionary information and uses those which contribute most to the overall performance. The introduced model is applied on several unimodal and multimodal continues problems. The experiments indicate that MaNet is able to yield competitive results compared to one of the best hand-designed algorithms for the aforementioned problems, in terms of the solution accuracy and scalability.

Demand for models that are "closer to biology" To carry out their interdisciplinary collaborative research, the scientists utilized data on locust behaviour from the Cluster of Excellence "Centre for the Advanced Study of Collective Behaviour" in Konstanz, which carries out internationally leading research on collective behaviour and is being funded through the German Excellence Strategy since the beginning of 2019. Biologists in particular are demanding that models explaining collective behaviour be designed to be "closer to biology." Most current models were devised by physicists who assume that interacting individuals are influenced by a physical force. As a result, they don't necessarily perceive individuals within swarms to be agents, but instead, as points such as interacting magnetization units on a grid. "The models work well in physics and have a good empirical basis there. However, they do not model the interaction between living individuals," says Thomas Müller.

We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists). Given a set of examples that are pairwise ordinal preferences over a universe of objects built of attributes of discrete values, we want to learn (1) an optimal LP-list that decides the maximum number of these examples, or (2) a near-optimal LP-list that decides as many examples as it can. To this end, we introduce a dynamic programming based algorithm and a genetic algorithm for these two learning problems, respectively. Furthermore, we empirically demonstrate that the sub-optimal models computed by the genetic algorithm very well approximate the de facto optimal models computed by our dynamic programming based algorithm, and that the genetic algorithm outperforms the baseline greedy heuristic with higher accuracy predicting new preferences.

Machine learning has been emerging as a promising tool in the chemical and materials domain. In this paper, we introduce a framework to automatically perform rational model selection and hyperparameter optimization that are important concerns for the efficient and successful use of machine learning, but have so far largely remained unexplored by this community. The framework features four variations of genetic algorithm and is implemented in the chemml program package. Its performance is benchmarked against popularly used algorithms and packages in the data science community and the results show that our implementation outperforms these methods both in terms of time and accuracy. The effectiveness of our implementation is further demonstrated via a scenario involving multi-objective optimization for model selection.

A lot of real-world applications could be modeled as the Mixed-Integer Non-Linear Programming (MINLP) problems, and some prominent examples include portfolio optimization, resource allocation, image classification, as well as path planning. Actually, most of the models for these applications are non-convex and always involve some conflicting objectives. Hence, the Multi-Objective Evolutionary Algorithm (MOEA), which does not require the gradient information and is efficient at dealing with the multi-objective optimization problems, is adopted frequently for these problems. In this work, we discuss the coding scheme for MOEA in MINLP, and the major discussion focuses on the constrained portfolio optimization problem, which is a classic financial problem and could be naturally modeled as MINLP. As a result, the challenge, faced by a direct coding scheme for MOEA in MINLP, is pointed out that the searching in multiple search spaces is very complicated. Thus, a Compressed Coding Scheme (CCS), which converts an MINLP problem into a continuous problem, is proposed to address this challenge. The analyses and experiments on 20 portfolio benchmark instances, of which the number of available assets ranging from 31 to 2235, consistently indicate that CCS is not only efficient but also robust for dealing with the constrained multi-objective portfolio optimization.

Quantum inspired Evolutionary Algorithms were proposed more than a decade ago and have been employed for solving a wide range of difficult search and optimization problems. A number of changes have been proposed to improve performance of canonical QEA. However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters. It is well known that performance of evolutionary algorithms is dependent on specific value of parameters for a given problem. The advantage of having large number of parameters in an operator is that the search process can be made more powerful even with a single operator without requiring a combination of other operators for exploration and exploitation. However, the tuning of operators with large number of parameters is complex and computationally expensive. This paper proposes a novel heuristic method for tuning parameters of canonical QEA. The tuned QEA outperforms canonical QEA on a class of discrete combinatorial optimization problems which, validates the design of the proposed parameter tuning framework. The proposed framework can be used for tuning other algorithms with both large and small number of tunable parameters.