Starfish Search

Starfish Search is a swarm optimization algorithm that operates in the same vein as Particle Swarm Optimization and the Firefly Algorithm. This search algorithm attempts to find global optimal solutions to optimization problems by dispersing agents into the search space. Each agent consists of many nodes that represent candidate solutions to the problem being solved. Agent\u27s nodes are formatted in a parent-child hierarchy, similar to tree structures, which facilitates information passing to a root node. With this structure, it becomes possible to determine the likely direction in which an optimal lies. By using a form of linear regression, the fitness values and positions of each node in an agent are used to evaluate a vector, known as the Local Gradient. This vector points along the slope of the search space, and its magnitude represents the steepness of this slope. In this way, an agent has an understanding of the local area and can make intelligent decisions about which direction to search for additional candidate solutions. With this additional information, agents also have the ability to execute behaviors based on the type of topology encountered. These behaviors can be specifically tailored to individual problems and situations to help agents correctly solve the problem. Starfish Search has been applied to problems such as, search space optimization, k nearest neighbors classification, and k means clustering. By tailoring fitness functions and behavior execution, evidence has been gathered to support the algorithms use over traditional techniques. This paper dives into the details of the algorithm\u27s implementation, calculations, and behaviors as well as explain the tests and evidence gathered to support the use of Starfish Search

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification

MIMR-DGSA: unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm

Band selection plays an important role in hyperspectral data analysis as it can improve the performance of data analysis without losing information about the constitution of the underlying data. We propose a MIMR-DGSA algorithm for band selection by following the Maximum-Information-Minimum-Redundancy (MIMR) criterion that maximises the information carried by individual features of a subset and minimises redundant information between them. Subsets are generated with a modified Discrete Gravitational Search Algorithm (DGSA) where we definine a neighbourhood concept for feature subsets. A fast algorithm for pairwise mutual information calculation that incorporates variable bandwidths of hyperspectral bands called VarBWFastMI is also developed. Classification results on three hyperspectral remote sensing datasets show that the proposed MIMR-DGSA performs similar to the original MIMR with Clonal Selection Algorithm (CSA) but is computationally more efficient and easier to handle as it has fewer parameters for tuning