Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion

This article explores deep reinforcement learning (DRL) for the flocking control of unmanned aerial vehicle (UAV) swarms. The flocking control policy is trained using a centralized-learning-decentralized-execution (CTDE) paradigm, where a centralized critic network augmented with additional information about the entire UAV swarm is utilized...

Risk-based optimization of emergency response systems for accidental gas leakage in utility tunnels

Focusing on the effective configuration of emergency response systems in utility tunnels, this study proposes an innovative approach to optimize existing emergency response systems based on a consequence rapid prediction model and genetic algorithm. In the proposed approach, the interactions between different emergency response components are...

Group-Based Distributed Auction Algorithms for Multi-Robot Task Assignment

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a...

A safety investment optimization model for power grid enterprises based on System Dynamics and Bayesian network theory

In recent years, frequent large-scale power grid accidents have caused serious economic losses and bad social impact, which has drawn great attention from power grid enterprises. As one of the key elements of production, safety investment plays an important role in improving the safety level and reducing accident loss. In this paper, System...

Dynamic resilience assessment and emergency strategy optimization of natural gas compartments in utility tunnels

As a kind of critical infrastructure of energy transportation, so-called ‘utility tunnels’ have been developed around the world. Hosting a natural gas pipeline inside the natural gas compartment of a utility tunnel facilitates its maintenance but also brings potential explosion concerns due to the confined space. Although some work focuses on...

On Some Optimization Problems that Can Be Solved in O(n) Time

We consider nine elementary problems in optimization. We simply explore the conditions for optimality as known from the duality theory for convex optimization. This yields a quite straightforward solution method for each of these problems. The main contribution of this paper is that we show that even in the harder cases the solution needs...

Power flow control in a substation of a wind- and solar farm: Design of an Optimisation Unit Considering a System's Physical Boundaries and Technical Constraints

The goal of this graduation project was to design a state-of-the-art central farm controller. The designed central farm controller distinguishes itself from prevailing controllers by including a subsystem (the optimisation unit) that contributes to an improvement in power transmission effciency, decrease in maintenance costs and an increase in...

Parameter optimization of environmental technologies using a LCA-based analysis scheme: A bioaugmentation case study

Life cycle assessment (LCA) has proven to be a useful tool in assessing environmental technologies in a retrospective manner. To fully uncover the environmental improvement potential while advancing technologies under technical and environmental constraints, this study recommended approaching the LCA proactively to assess the progress of...

Improved Full-Newton Step O(nL) Infeasible Interior-Point Method for Linear Optimization

We present several improvements of the full-Newton step infeasible interior-point method for linear optimization introduced by Roos (SIAM J. Optim. 16(4):1110–1136, 2006). Each main step of the method consists of a feasibility step and several centering steps. We use a more natural feasibility step, which targets the ?+-center of the next pair...

Kernel-function based algorithms for semidefinitie optimization

Recently, Y.Q. Bai, M. El Ghami and C. Roos [3] introduced a new class of so-called eligible kernel functions which are defined by some simple conditions. The authors designed primal-dual interiorpoint methods for linear optimization (LO) based on eligible kernel functions and simplified the analysis of these methods considerably. In this paper...

A Class of Large-Update and Small-Update Primal-Dual Interior-Point Algorithms for Linear Optimization

In this paper we present a class of polynomial primal-dual interior-point algorithms for linear optimization based on a new class of kernel functions. This class is fairly general and includes the classical logarithmic function, the prototype self-regular function, and non-self-regular kernel functions as special cases. The analysis of the...