A genetic algorithm for optimal flow assignment in computer network

Multi-object tracking (MOT) plays a very important role in advanced applications such as driver assistance systems, autonomous vehicles, aircraft monitoring systems. One of the fundamental problems of multi-object tracking is the unknown data association. At each time step, we get multiple sensor measurements. However, we don't know where these sensor measurements come from. Each measurement can come from one of the objects detected in the previous time step, from a new object entering the field of view (FOV), or from clutter. As the number of objects and measurements increases, the combinatorial nature causes the number of data association hypotheses to increase rapidly, making it impossible to consider all data associations [1,2]. One approach to solving the data association problem is to transform it into an optimal assignment problem. One approach to solving the data association problem is to use algorithms of the optimal assignment problem. In this paper, we will introduce the method of using optimal assignment algorithms to solve the data association problem in MOT. At the same time, we also compare the performance of these algorithms in terms of data association accuracy and computational complexity. The results showed that using assignment algorithms like Gibbs sampling brings high performance in data association.

The next generation multimedia applications such as video conferencing and HDTV have raised tremendous challenges on the network design, both in bandwidth and service. As wavelength-division-multiplexing (WDM) networks have emerged as a promising candidate for future networks with large bandwidth, supporting efficient multicast in WDM networks becomes eminent. Different from the IP layer, the cost of multicast at the WDM layer involves not only bandwidth (wavelength) cost, but also wavelength conversion cost and light splitting cost. It is well known that the optimal multicast problem in WDM networks is NP-hard. In this paper, we develop an efficient approximation algorithm consisting of two separate but integrated steps: multicast routing and wavelength assignment. We prove that the problem of optimal wavelength assignment on a multicast tree is not NP-hard; in fact, an optimal wavelength assignment algorithm with complexity of O(NW) is presented. Simulation results have revealed that the optimal wavelength assignment beats greedy algorithms by a large margin in networks using many wavelengths on each link such as dense wavelength-division-multiplexing (DWDM) networks. Our proposed heuristic multicast routing algorithm takes into account both the cost of using wavelength on links and the cost of wavelength conversion. The resulting multicast tree is derived from the optimal lightpaths used for unicast.

Recently, the installation of the battery for the power system has been investigated. This study focuses on the optimal battery assignment method which assigns the batteries to contribute to the system stabilization against N-1 line accident. In this method, the optimal battery assignment and operation problem whose objective is the minimization of the phase difference fluctuation on all nodes caused by N-1 line accident at each branch is formulated. Then, the optimal battery assignment and the power supplies of batteries are determined by solving the formulated problem. However, this method requires a lot of computation time, because this method requires a large number of power flow calculations with high computational cost. This study proposes to apply the response surface method with the radial basis function networks to the optimal battery assignment and operation problem in order to reduce the number of the power flow calculations. This paper confirms the effectiveness of the proposed method through numerical experiments of the application to the IEEJ EAST 10-machine system model.

For improving the effect of multi-target tracking in dense target and clutter scenario, a centralized fuzzy optimal assignment algorithm (CMS-FOA) of three-sensor multi-target system is proposed. And on the base of this, a generalized probabilistic data association algorithm (CMS-FOAGPDA) based on CMS-FOA algorithm is presented. The fusion algorithm gets effective 3-tuple of measurement set by using components of several satisfactory solutions of the fuzzy optimal assignment problem and then uses generalized probabilistic data association algorithm to calculate the update states of targets. Simulation results show that, in the aspect of multi-target tracking accuracy, CMS-FOA algorithm is superior to the optimal assignment (CMS-OA) algorithm based on state estimate and CMS-FOAGPDA algorithm is better than CMS-FOA algorithm. But considering the time spent, CMS-FOA algorithm spends a minimum of time and CMS-FOAGPDA algorithm is exactly on the contrary. Therefore, compared with CMS-OA algorithm, the two algorithms presented in the study each has its advantages and should be chosen according to the needs of the actual application when in use.

This paper presents a distributed, guidance and control algorithm for reconfiguring swarms composed of hundreds to thousands of agents with limited communication and computation capabilities. This algorithm solves both the optimal assignment and collision-free trajectory generation for robotic swarms, in an integrated manner, when given the desired shape of the swarm (without pre-assigned terminal positions). The optimal assignment problem is solved using a distributed auction assignment that can vary the number of target positions in the assignment, and the collision-free trajectories are generated using sequential convex programming. Finally, model predictive control is used to solve the assignment and trajectory generation in real time using a receding horizon. The model predictive control formulation uses current state measurements to resolve for the optimal assignment and trajectory. The implementation of the distributed auction algorithm and sequential convex programming using model predictive control produces the swarm assignment and trajectory optimization (SATO) algorithm that transfers a swarm of robots or vehicles to a desired shape in a distributed fashion. Once the desired shape is uploaded to the swarm, the algorithm determines where each robot goes and how it should get there in a fuel-efficient, collision-free manner. Results of flight experiments using multiple quadcopters show the effectiveness of the proposed SATO algorithm.

The problem of state feedback optimal pole assignment is to design a feedback gain such that the closed-loop system has desired eigenvalues and such that certain quadratic performance index is minimized. Optimal pole assignment controller can guarantee both good dynamic response and well robustness properties of the closed-loop system. With the help of a class of linear matrix equations, necessary and sufficient conditions for the existence of a solution to the optimal pole assignment problem are proposed in this paper. By properly choosing the free parameters in the parametric solutions to this class of linear matrix equations, complete solutions to the optimal pole assignment problem can be obtained. A numerical example is used to illustrate the effectiveness of the proposed approach.

A Genetic Algorithm for Optimal Flow Assignment in Computer Network

Audsley's optimal priority assignment (OPA) algorithm can be applied to multiprocessor scheduling provided that three conditions hold with respect to the schedulability tests used. In this short paper, we prove that no exact test for global fixed priority pre-emptive scheduling of sporadic tasks can be compatible with Audsley's algorithm, and hence the OPA algorithm cannot be used to obtain an optimal priority assignment for such systems.

This chapter considers the optimal network flow problem, which is a generalization of the optimal assignment problem considered in Chapter 3. In optimal flow problems, one considers a network of cities, or edges, to move a distribution of mass on supply nodes to a distribution of mass on demand nodes. The difference from a standard optimal assignment problem is that the matching surplus associated with moving from a supply location to a demand location is not necessarily directly defined; instead, there are several paths from the supply location to the demand location, among these some yield maximal surplus. Therefore, both the optimal assignment problem and the shortest path problem are instances of the optimal flow problem; these instances are representative in the sense that any optimal flow problem may be decomposed into an assignment problem and a number of shortest path problems. The chapter shows how to easily compute these problems using linear programming.

In the underlay cognitive radio networks, this paper defines the joint channel and power allocation problem, which aims to optimise the max-total and max-min throughputs of secondary users (SUs), with the constraints of interference on primary receivers. For the max-total problem, we formulate the problem as a bipartite matching and derive a maximum weighted matching-based sum throughput maximisation algorithm (STMA) to solve this problem. For the max-min problem, on the basis of the optimal relay assignment (ORA) algorithm, we derive a polynomial time optimal channel assignment algorithm (OCAA) to iteratively assign channels to each SU pair under the power constraint. Simulation results demonstrate the effectiveness of our algorithms when compared with random method.

Recently, the installation of the battery for the power system has been expected to be one of action plans for the system stabilization against the voltage fluctuation caused by the system disturbance under the penetration of the renewable energy sources. This study proposes a new optimal battery assignment method considering the voltage stability under the penetration of the renewable energy sources. This study considers power flow conditions that the power supply from the renewable energy sources connecting to one node is disrupted, but the other sources supply the rated power. Then, an optimal battery assignment and operation problem whose objective is maximization of the voltage stability for all nodes in the system against any renewable energy supply disruption in the system is formulated. This study proposes to apply an optimization method using the tabu search and the differential evolution in order to solve the formulated problem. The effectiveness of the proposed method is confirmed through numerical experiments with IEEJ EAST 10-machine system model.

Considers the optimal (i.e., minimum length) time slot assignment problem for variable bandwidth switching systems. Existing algorithms for this problem are known to be pseudo-polynomial. The practical question of finding a fast optimal algorithm, as well as the theoretical question of whether the above problem is NP-complete were left open. The authors present a technique to show polynomial time complexity of some time slot assignment algorithms. Such a technique applies to an algorithm proposed by Chalasani and Varma in 1991 (called the CV algorithm), as well as to a network flow based optimal algorithm, proposed in the present paper for the first time. The CV algorithm and the one proposed are slightly different. Thus, the authors give an answer to both the above questions, by establishing that the problem is in P, and by showing effective algorithms for it.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper deals with a deterministic inventory model developed for deteriorating items having two separate storage facilities (owned and rented warehouses) due to limited capacity of the existing storage (owned warehouse) with linear time dependent demand (increasing) over a fixed finite time horizon. The model is formulated with infinite replenishment and the successive replenishment cycle lengths are in arithmetic progression. Partially backlogged shortages are allowed. The stocks of rented warehouse (RW) are transported to the owned warehouse (OW) in continuous release pattern. For this purpose, the model is formulated as a constrained non-linear mixed integer programming problem. For solving the problem, an advanced genetic algorithm (GA) has been developed. This advanced GA is based on ranking selection, elitism, whole arithmetic crossover and non-uniform mutation dependent on the age of the population. Our objective is to determine the optimal replenishment number, lot-size of two-warehouses (OW and RW) by maximizing the profit function. The model is illustrated with four numerical examples and sensitivity analyses of the optimal solution are performed with respect to different parameters.

This paper presents an approach to determine the optimal Genetic Algorithm (GA), i.e. the most preferable type of genetic operators and their parameter settings, for a given problem. The basic idea is to consider the search for the best GA as an optimization problem and use another GA to solve it. As a consequence, a primary GA operates on a population of secondary GAs which in turn solve the problem in discussion. The paper describes how to encode the relevant information about GAs in gene strings and analyzes the impact of the individual genes on the results produced. The feasibility of the approach is demonstrated by presenting a parallel implementation on a multi-transputer system. Performance results for finding the best GA for the problem of optimal weight assignment in feedforward neural networks are presented.

A graph-based approach to finding near-optimal output phase assignments for PLA minimization is presented. A distinctive feature of the approach is that it exploits the necessary and sufficient conditions to reduce the number of product terms needed for PLA implementation and permits the use of existing graph algorithms to solve the PLA output phase optimization problem. The work is based on the transformation of a PLA into a graph whose vertices are the set of conditions required to reduce each product term of the PLA, and whose edges represent the relations between these conditions. Cliques (completely connected subgraphs) in the graph correspond to the output phase assignments required to reduce the product terms represented by the vertices in the cliques. The optimal PLA output phase assignment problem is then formulated as the well-studied problem in graph theory: finding maximum cliques in graphs. Using an existing algorithm for locating cliques in graphs, a modified greedy algorithm is proposed to compute output phase assignments for logic functions in polynomial time. Experimental results using a number of benchmark functions show that the graph-based approach can achieve optimal or near-optimal output phase assignment for PLA minimization and can lead to PLA's with fewer product terms than achieved using existing approaches.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>