A Chaotic Particle Swarm Optimization (CPSO) Algorithm for Solving Optimal Reactive Power Dispatch Problem

This paper presents a chaotic particle swarm algorithm for solving the multi-objective reactive power dispatch problem. To deal with reactive power optimization problem, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively.  In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and simulation results show that (CPSO)   is more efficient than other algorithms in reducing the real power loss and maximization of voltage stability index. Keywords:chaotic particle swarm optimization, Optimization, Swarm Intelligence, optimal reactive power, Transmission loss

Data-Driven Predictive Modeling to Enhance Search Efficiency of Glowworm-Inspired Robotic Swarms in Multiple Emission Source Localization Tasks

In time-sensitive search and rescue applications, a team of multiple mobile robots broadens the scope of operational capabilities. Scaling multi-robot systems (\u3c 10 agents) to larger robot teams (10 – 100 agents) using centralized coordination schemes becomes computationally intractable during runtime. One solution to this problem is inspired by swarm intelligence principles found in nature, offering the benefits of decentralized control, fault tolerance to individual failures, and self-organizing adaptability. Glowworm swarm optimization (GSO) is unique among swarm-based algorithms as it simultaneously focuses on searching for multiple targets. This thesis presents GPR-GSO—a modification to the GSO algorithm that incorporates Gaussian Process Regression (GPR) based data-driven predictive modeling—to improve the search efficiency of robotic swarms in multiple emission source localization tasks. The problem formulation and methods are presented, followed by numerical simulations to illustrate the working of the algorithm. Results from a comparative analysis show that the GPR-GSO algorithm exceeds the performance of the benchmark GSO algorithm on evaluation metrics of swarm size, search completion time, and travel distance

Novel Constellation Design Tool: A Framework for Asymmetric Constellation Design

This paper addresses the problem of optimization of earth orbit space vehicle constellations balancing access over specified earth regions subject to constraints on the constituent members’ orbital elements. Whereas symmetric constellations such as Walker Delta Parameter Constellations are often a convenient starting point for system trade studies, the desire to balance access over multiple earth latitude bands requires exploration of asymmetric constellations. This paper proposes a simple, yet effective method to rapidly test asymmetric constellation designs incorporating arbitrary constraints on the space vehicles’ orbital elements. A user of this novel Constellation Design Tool (CDT) provides inputs to the code, including details about desired vehicle altitudes, range of inclinations, number of space vehicles per orbital plane, and number of total space vehicles within the constellation, as well as the desired set of latitude, longitude and altitude points to which space vehicle access is to be tested. The first stage of the CDT executes a Monte Carlo simulation using pseudorandom generations of constellation designs, providing the user with the Keplerian elements of the top-performing constellations subject to a user-defined figure of merit. Subsequently, the second stage of the CDT, drawing inspiration from a particle swarm optimization method, makes incremental changes to the orbital elements, testing the reported performance of the constellation against all other variations of the base constellation. After completion of a specified number of iterations, the top-performing constellation’s orbital elements are loaded into STK with the replicated simulation environment to further analyze the constellation and provide performance data to the user

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners