scholarly journals Comparison of the efficiency of two algorithms which solve the shortest path problem with an emotional agent

2006 ◽  
Vol 16 (2) ◽  
pp. 211-226 ◽  
Author(s):  
Silvana Petruseva
Keyword(s):  
Shortest Path ◽  
Polynomial Algorithm ◽  
Optimal Solution ◽  
Agent Architecture ◽  
Multiplicative Constant ◽  
Goal State ◽  
The Neural Network ◽  
Starting State ◽  
Agent Learning ◽  
One Step

This paper discusses the comparison of the efficiency of two algorithms, by estimation of their complexity. For solving the problem, the Neural Network Crossbar Adaptive Array (NN-CAA) is used as the agent architecture, implementing a model of an emotion. The problem discussed is how to find the shortest path in an environment with n states. The domains concerned are environments with n states, one of which is the starting state, one is the goal state, and some states are undesirable and they should be avoided. It is obtained that finding one path (one solution) is efficient, i.e. in polynomial time by both algorithms. One of the algorithms is faster than the other only in the multiplicative constant, and it shows a step forward toward the optimality of the learning process. However, finding the optimal solution (the shortest path) by both algorithms is in exponential time which is asserted by two theorems. It might be concluded that the concept of subgoal is one step forward toward the optimality of the process of the agent learning. Yet, it should be explored further on, in order to obtain an efficient, polynomial algorithm.

scholarly journals Emotion Learning: Solving a Shortest Path Problem in an Arbitrary Deterministic Environment in Linear Time with an Emotional Agent

2008 ◽  
Vol 18 (3) ◽  
pp. 409-421 ◽  
Author(s):  
Silvana Petruseva
Keyword(s):  
Shortest Path ◽  
Emotional State ◽  
Linear Time ◽  
Decisive Role ◽  
Time Algorithm ◽  
Shortest Path Problem ◽  
Adaptive Array ◽  
Agent Architecture ◽  
Initial State ◽  
Goal State

Emotion Learning: Solving a Shortest Path Problem in an Arbitrary Deterministic Environment in Linear Time with an Emotional AgentThe paper presents an algorithm which solves the shortest path problem in an arbitrary deterministic environment withnstates with an emotional agent in linear time. The algorithm originates from an algorithm which in exponential time solves the same problem, and the agent architecture used for solving the problem is an NN-CAA architecture (neural network crossbar adaptive array). By implementing emotion learning, the linear time algorithm is obtained and the agent architecture is modified. The complexity of the algorithm without operations for initiation in general does not depend on the number of statesn, but only on the length of the shortest path. Depending on the position of the goal state, the complexity can be at mostO (n).It can be concluded that the choice of the function which evaluates the emotional state of the agent plays a decisive role in solving the problem efficiently. That function should give as detailed information as possible about the consequences of the agent's actions, starting even from the initial state. In this way the function implements properties of human emotions.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

scholarly journals Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin Uncertainty

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3791
Author(s):  
Tianli Ma ◽  
Song Gao ◽  
Chaobo Chen ◽  
Xiaoru Song
Keyword(s):  
Shortest Path ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Tracking Algorithm ◽  
Multitarget Tracking ◽  
Integer Programming Problem ◽  
Tracking Accuracy ◽  
Network Graph ◽  
Adaptive Network

To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nyström Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch–Tung–Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm.

Planning the Shortest Path in Cluttered Environments: A Review and a Planar Convex Hull-Based Approach

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Nafiseh Masoudi ◽  
Georges M. Fadel ◽  
Margaret M. Wiecek
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Time Complexity ◽  
Optimal Path ◽  
Optimal Solution ◽  
Free Graph ◽  
Worst Case ◽  
Cluttered Environments ◽  
Planar Convex ◽  
Polyhedral Obstacles

Abstract Routing or path-planning is the problem of finding a collision-free and preferably shortest path in an environment usually scattered with polygonal or polyhedral obstacles. The geometric algorithms oftentimes tackle the problem by modeling the environment as a collision-free graph. Search algorithms such as Dijkstra’s can then be applied to find an optimal path on the created graph. Previously developed methods to construct the collision-free graph, without loss of generality, explore the entire workspace of the problem. For the single-source single-destination planning problems, this results in generating some unnecessary information that has little value and could increase the time complexity of the algorithm. In this paper, first a comprehensive review of the previous studies on the path-planning subject is presented. Next, an approach to address the planar problem based on the notion of convex hulls is introduced and its efficiency is tested on sample planar problems. The proposed algorithm focuses only on a portion of the workspace interacting with the straight line connecting the start and goal points. Hence, we are able to reduce the size of the roadmap while generating the exact globally optimal solution. Considering the worst case that all the obstacles in a planar workspace are intersecting, the algorithm yields a time complexity of O(n log(n/f)), with n being the total number of vertices and f being the number of obstacles. The computational complexity of the algorithm outperforms the previous attempts in reducing the size of the graph yet generates the exact solution.

CONTROL STRATEGIES FOR CHAOTIC NEUROMODULES

1996 ◽  
Vol 06 (04) ◽  
pp. 693-703 ◽  
Author(s):  
NICO STOLLENWERK ◽  
FRANK PASEMANN
Keyword(s):  
Least Squares ◽  
Chaotic Dynamics ◽  
Chaotic Systems ◽  
Least Squares Method ◽  
Control Strategies ◽  
Nonlinear Least Squares ◽  
The Neural Network ◽  
One Step ◽  
Nonlinear Least Squares Method ◽  
The One

Different strategies for control of chaotic systems are discussed: The well known Ott-Grebogi-Yorke algorithm and two alternative algorithms based on least-squares minimisation of the one step future deviation. To compare their effectiveness in the neural network context they are applied to a minimal two neuron module with discrete chaotic dynamics. The best method with respect to calculation effort, to neural implementation, and to controlling properties is the nonlinear least squares method. Furthermore, it is observed in simulations that one can stabilise a whole periodic orbit by applying the control signals only to one of its periodic points, which lies in a distinguished region of phase space.

scholarly journals Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

2021 ◽  
Vol 2 (1) ◽  
pp. 45-52
Author(s):  
Muhammad Rhifky Wayahdi ◽  
Subhan Hafiz Nanda Ginting ◽  
Dinur Syahputra
Keyword(s):  
Greedy Algorithm ◽  
Shortest Path ◽  
Optimal Solution ◽  
The Other ◽  
Complex Data ◽  
Dijkstra’S Algorithm ◽  
Dijkstra's Algorithm ◽  
The Greedy Algorithm ◽  
Better Than

The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.

Research on Multimodal Transport Shortest Path Based on the Genetic Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1813-1816
Author(s):  
Shou Wen Ji ◽  
Shi Jin ◽  
Kai Lv
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shortest Path ◽  
Optimal Solution ◽  
Path Model ◽  
Time Analysis ◽  
Multimodal Transportation ◽  
Network Time ◽  
Multimodal Transport ◽  
Crossover And Mutation

This paper focuses on the research of multimodal transportation optimization model and algorithm, designs an intermodal shortest time path model and gives a solution to algorithm, constructs a multimodal transport network time analysis chart. By using genetic algorithms, the transportation scheme will be optimized. And based on each path’s code, the population will be evolved to obtain the optimal solution by using crossover and mutation rules.

A Network Science-based performance improvement model for the airline industry using NetworkX

2020 ◽  
Vol 10 ◽  
Author(s):  
Vishnu Vardhan Reddy Kollu ◽  
Shanmuk Srinivas Amiripalli ◽  
Mukkamala S N V Jitendra ◽  
T. Ravi Kumar
Keyword(s):  
Shortest Path ◽  
Airline Industry ◽  
Network Science ◽  
Optimal Solution ◽  
Clustering Coefficient ◽  
Theoretical Research ◽  
Data Set ◽  
Non Profit ◽  
Challenging Situation ◽  
Optimum Solution

Background:: COVID 19 created a challenging situation for many of the industries, in this paper one among them is the airline industry was addressed by theoretical research for better connectivity and profits. In our previous work, two Airlines were analyzed and it was observed that adding trips to a non-profit airline with respect to profit Airlines is one the optimal technique to improve the performance. In this paper, multi-airlines are considered with three companies. Methods:: In the first step collect data set about these 3 companies and convert them into graphs. Apply network Science parameters like diameter, density, average degree, clustering coefficient, the shortest path of these 3 graphs are generated. Next step analyses on these parameters were Perform and identified the profitable Airlines. The proposed algorithms will apply either trimming or adding operations on low-profit airline operators with respective to the profitable Airlines. In the last phase, proposed algorithm will generate an output with better connectivity and profits. Results:: In this research other interesting findings, which are quite contrasted to the previous findings, were observed, that current research says trimming of trips to non-profit Airlines with respect to the profit Airlines can also be an optimal solution for better performance. Discussion:: In this research Complex multigraph, airlines are Analysed by using graph Analytics technique for the optimum solution. In this research standard parameters like edges, nodes, degree, clustering, shortest path are compared on indigo, SpiceJet, and AirAsia airline systems. Conclusion: The proposed algorithm analyzes the connectivity of airline systems and applies either trimming or enhancing techniques. Indigo Airlines Was the best-connected network over the other two, so Only trimming operations will be performed on Indigo. for Air Asia and SpiceJet both trimming and enhancing will be performed the reference to Indigo Airlines.

A Progressive Approximation Approach for the Exact Solution of Sparse Large-Scale Binary Interdiction Games

2021 ◽  
Author(s):  
Claudio Contardo ◽  
Jorge A. Sefair
Keyword(s):  
Exact Solution ◽  
Approximation Algorithm ◽  
Facility Location ◽  
Shortest Path ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Solution Space ◽  
Decision Variables ◽  
Algorithmic Framework

We present a progressive approximation algorithm for the exact solution of several classes of interdiction games in which two noncooperative players (namely an attacker and a follower) interact sequentially. The follower must solve an optimization problem that has been previously perturbed by means of a series of attacking actions led by the attacker. These attacking actions aim at augmenting the cost of the decision variables of the follower’s optimization problem. The objective, from the attacker’s viewpoint, is that of choosing an attacking strategy that reduces as much as possible the quality of the optimal solution attainable by the follower. The progressive approximation mechanism consists of the iterative solution of an interdiction problem in which the attacker actions are restricted to a subset of the whole solution space and a pricing subproblem invoked with the objective of proving the optimality of the attacking strategy. This scheme is especially useful when the optimal solutions to the follower’s subproblem intersect with the decision space of the attacker only in a small number of decision variables. In such cases, the progressive approximation method can solve interdiction games otherwise intractable for classical methods. We illustrate the efficiency of our approach on the shortest path, 0-1 knapsack and facility location interdiction games. Summary of Contribution: In this article, we present a progressive approximation algorithm for the exact solution of several classes of interdiction games in which two noncooperative players (namely an attacker and a follower) interact sequentially. We exploit the discrete nature of this interdiction game to design an effective algorithmic framework that improves the performance of general-purpose solvers. Our algorithm combines elements from mathematical programming and computer science, including a metaheuristic algorithm, a binary search procedure, a cutting-planes algorithm, and supervalid inequalities. Although we illustrate our results on three specific problems (shortest path, 0-1 knapsack, and facility location), our algorithmic framework can be extended to a broader class of interdiction problems.

A One-Layer Recurrent Neural Network with a Discontinuous Activation Function for Linear Programming

2008 ◽  
Vol 20 (5) ◽  
pp. 1366-1383 ◽  
Author(s):  
Qingshan Liu ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Linear Programming ◽  
Recurrent Neural Network ◽  
Optimal Solution ◽  
Activation Function ◽  
High Gain ◽  
Discontinuous Activation ◽  
The Neural Network ◽  
Decision Variables ◽  
Discontinuous Activation Function

A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. It is proven that the neural network with a sufficiently high gain is globally convergent to the optimal solution. Its application to linear assignment is discussed to demonstrate the utility of the neural network. Several simulation examples are given to show the effectiveness and characteristics of the neural network.

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