A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network

The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become an inevitable trend. In this paper, a mixed integer programming model for observation tasks is established, and a heuristic search algorithm based on a symmetric recurrent neural network is proposed. The configurable probability of the observation task is obtained by constructing a structural symmetric recurrent neural network, and finally, the optimal task planning scheme is obtained. The experimental results are compared with several typical heuristic search algorithms, which have certain advantages, and the validity of the paper is verified. Finally, future application prospects of the method are discussed.

Download Full-text

Positive Macroscopic Approximation for Fast Attribute Reduction

Journal of Applied Mathematics ◽

10.1155/2013/837281 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11

Author(s):

Zheng-Cai Lu ◽

Zheng Qin ◽

Qiao Jing ◽

Lai-Xiang Shan

Keyword(s):

Heuristic Search ◽

Search Algorithm ◽

Attribute Reduction ◽

Boundary Region ◽

Feature Subset ◽

Decision Systems ◽

Heuristic Search Algorithms ◽

Heuristic Search Algorithm ◽

Single Target ◽

Lower And Upper Approximations

Attribute reduction is one of the challenging problems facing the effective application of computational intelligence technology for artificial intelligence. Its task is to eliminate dispensable attributes and search for a feature subset that possesses the same classification capacity as that of the original attribute set. To accomplish efficient attribute reduction, many heuristic search algorithms have been developed. Most of them are based on the model that the approximation of all the target concepts associated with a decision system is dividable into that of a single target concept represented by a pair of definable concepts known as lower and upper approximations. This paper proposes a novel model called macroscopic approximation, considering all the target concepts as an indivisible whole to be approximated by rough set boundary region derived from inconsistent tolerance blocks, as well as an efficient approximation framework called positive macroscopic approximation (PMA), addressing macroscopic approximations with respect to a series of attribute subsets. Based on PMA, a fast heuristic search algorithm for attribute reduction in incomplete decision systems is designed and achieves obviously better computational efficiency than other available algorithms, which is also demonstrated by the experimental results.

Download Full-text

Machine Learning Based Heuristic Search Algorithms to Solve Birds of a Feather Card Game

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019656 ◽

2019 ◽

Vol 33 ◽

pp. 9656-9661

Author(s):

Bryon Kucharski ◽

Azad Deihim ◽

Mehmet Ergezer

Keyword(s):

Machine Learning ◽

Undergraduate Students ◽

Heuristic Search ◽

Heuristic Algorithms ◽

Search Algorithm ◽

Search Algorithms ◽

Perfect Information ◽

Monte Carlo Tree Search ◽

Heuristic Search Algorithms ◽

Heuristic Search Algorithm

This research was conducted by an interdisciplinary team of two undergraduate students and a faculty to explore solutions to the Birds of a Feather (BoF) Research Challenge. BoF is a newly-designed perfect-information solitaire-type game. The focus of the study was to design and implement different algorithms and evaluate their effectiveness. The team compared the provided depth-first search (DFS) to heuristic algorithms such as Monte Carlo tree search (MCTS), as well as a novel heuristic search algorithm guided by machine learning. Since all of the studied algorithms converge to a solution from a solvable deal, effectiveness of each approach was measured by how quickly a solution was reached, and how many nodes were traversed until a solution was reached. The employed methods have a potential to provide artificial intelligence enthusiasts with a better understanding of BoF and novel ways to solve perfect-information games and puzzles in general. The results indicate that the proposed heuristic search algorithms guided by machine learning provide a significant improvement in terms of number of nodes traversed over the provided DFS algorithm.

Download Full-text

Fractional-atom search algorithm-based deep recurrent neural network for cancer classification

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03008-z ◽

2021 ◽

Author(s):

D. Menaga ◽

S. Revathi

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Search Algorithm ◽

Cancer Classification ◽

Deep Recurrent Neural Network

Download Full-text

Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

Applied Sciences ◽

10.3390/app11052175 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2175

Author(s):

Oscar Danilo Montoya ◽

Walter Gil-González ◽

Jesus C. Hernández

Keyword(s):

Objective Function ◽

Reactive Power ◽

Programming Model ◽

Search Algorithm ◽

Solution Space ◽

Distribution Networks ◽

Optimal Placement ◽

Mixed Integer ◽

Solution Vector ◽

The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

Download Full-text