A Hybrid Neural-Genetic Algorithm for the Frequency Assignment Problem in Satellite Communications

A hybrid Neural-Genetic algorithm (NG) is presented for the frequency assignment problem in satellite communications (FAPSC). The goal of this problem is minimizing the cochannel interference between satellite communication systems by rearranging the frequency assignments. Previous approaches to FAPSC show lack of scalability, which leads to poor results when the size of the problem grows. The NG algorithm consists of a Hopfield neural network which manages the problem constraints hybridized with a genetic algorithm for improving the solutions obtained. This separate management of constraints and optimization of objective function gives the NG algorithm the properties of scalability required.We analyze the FAPSC and its formulation, describe and discuss the NG algorithm and solve a set of benchmark problems. The results obtained are compared with other existing approaches in order to show that the NG algorithm is more scalable and performs better than previous algorithms in the FAPSC.This work was supported in part by CICYT under grant TIC 1999-0216     

A hybrid approach based on stochastic competitive Hopfield neural network and efficient genetic algorithm for frequency assignment problem

This paper presents a hybrid efficient genetic algorithm (EGA) for the stochastic competitive Hopfield (SCH) neural network, which is named SCH–EGA. This approach aims to tackle the frequency assignment problem (FAP). The objective of the FAP in satellite communication system is to minimize the co-channel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate increasing demands. Our hybrid algorithm involves a stochastic competitive Hopfield neural network (SCHNN) which manages the problem constraints, when a genetic algorithm searches for high quality solutions with the minimum possible cost. Our hybrid algorithm, reflecting a special type of algorithm hybrid thought, owns good adaptability which cannot only deal with the FAP, but also cope with other problems including the clustering, classification, and the maximum clique problem, etc. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm. Then we explore three hybridizations between SCHNN and EGA to discover the best hybrid algorithm. We believe that the comparison can also be helpful for hybridizations between neural networks and other evolutionary algorithms such as the particle swarm optimization algorithm, the artificial bee colony algorithm, etc. In the experiments, our hybrid algorithm obtains better or comparable performance than other algorithms on 5 benchmark problems and 12 large problems randomly generated. Finally, we show that our hybrid algorithm can obtain good results with a small size population.     

Multi-start stochastic competitive Hopfield neural network for frequency assignment problem in satellite communications

The objective of the frequency assignment problem (FAP) is to minimize cochannel interference between two satellite systems by rearranging frequency assignment. In this paper, we first propose a competitive Hopfield neural network (CHNN) for FAP. Then we propose a stochastic CHNN (SCHNN) for the problem by introducing stochastic dynamics into the CHNN to help the network escape from local minima. In order to further improve the performance of the SCHNN, a multi-start strategy or re-start mechanism is introduced into the SCHNN. The multi-start strategy or re-start mechanism super-imposed on the SCHNN is characterized by alternating phases of cooling and reheating the stochastic dynamics, thus provides a means to achieve an effective dynamic or oscillating balance between intensification and diversification during the search. Furthermore, dynamic weighting coefficient setting strategy is adopted in the energy function to satisfy the constraints and improve the objective of the problem simultaneously. The proposed multi-start SCHNN (MS-SCHNN) is tested on a set of benchmark problems and a large number of randomly generated instances. Simulation results show that the MS-SCHNN is better than several typical neural network algorithms such as GNN, TCNN, NCNN and NCNN-VT, and metaheuristic algorithm such as hybrid SA.     

A portable and scalable algorithm for a class of constrained combinatorial optimization problems

This paper presents a portable and scalable approach for a class of constrained combinatorial optimization problems (CCOPs) which requires to satisfy a set of constraints and to optimize and objective function simultaneously. In particular, this paper is focused on the class of CCOPs that admits a representation in terms of a square matrix of constraints C.The algorithm consists of a hybrid neural-genetic algorithm, formed by a Hopfield Neural Network (HNN) which solves the problem's constraints, and a Genetic Algorithm (GA) for optimizing the objective function. This separated management of constraints and optimization procedures makes the proposed algorithm scalable and robust. The portability of the algorithm is given by the fact that the HNN dynamics depends only on the matrix C of constraints.We show these properties of scalability and portability by solving three different CCOPs with our algorithm, the frequency assignment problem in a mobile telecommunications network, the reduction of the interference in satellite systems and the design of FPGAs with segmented channel routing architecture. We compare our results with previous approaches to these problems, obtaining very good results in all of them.     

Optimized planning of frequency hopping in cellular networks

We consider a generalization of the classical frequency assignment problem. The generalization arises when frequency hopping is used in a cellular network. The planning problem concerns assigning lists of frequencies to blocks of transceivers, such that the total interference is minimized. This problem is considerably more difficult than the classical frequency assignment problem, because of the large number of possible frequency lists. We provide the technical background that motivates our study, and present a mathematical model which includes the classical frequency assignment problem as a special case. We describe a simulated annealing algorithm. The algorithm explores the solution space by solving an integer program in each iteration. We report computational results for real-life and synthesized networks.     

A hybrid Hopfield network-genetic algorithm approach for the terminal assignment problem

This paper presents a hybrid Hopfield network-genetic algorithm (GA) approach to tackle the terminal assignment (TA) problem. TA involves determining minimum cost links to form a communications network, by connecting a given set of terminals to a given collection of concentrators. Some previous approaches provide very good results if the cost associated with assigning a single terminal to a given concentrator is known. However, there are situations in which the cost of a single assignment is not known in advance, and only the cost associated with feasible solutions can be calculated. In these situations, previous algorithms for TA based on greedy heuristics are no longer valid, or fail to get feasible solutions. Our approach involves a Hopfield neural network (HNN) which manages the problem's constraints, whereas a GA searches for high quality solutions with the minimum possible cost. We show that our algorithm is able to achieve feasible solutions to the TA in instances where the cost of a single assignment in not known in advance, improving the results obtained by previous approaches. We also show the applicability of our approach to other problems related to the TA.     

Task assignment for minimizing application completion time using honeybee mating optimization

Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique for minimizing the parallel application time cost of task assignment based on the honeybee mating optimization (HBMO) algorithm. The HBMO approach combines the power of simulated annealing, genetic algorithms, and an effective local search heuristic to find the best possible solution to the problem within an acceptable amount of computation time. The performance of the proposed HBMO algorithm is shown by comparing it with three existing task assignment techniques on a large number of randomly generated problem instances. Experimental results indicate that the proposed HBMO algorithm outperforms the competing algorithms.     

Assignment of cells to switches in a cellular mobile network using a hybrid Hopfield network-genetic algorithm approach

Handoff and cabling cost management plays a key role in the design of cellular telecommunications networks. The efficient assignment of cells to switches in this type of networks is an NP-complete problem which cannot be solved efficiently unless P = NP. This paper presents a hybrid Hopfield network-genetic algorithm approach to the cell-to-switches assignment problem, in which a Hopfield network manages the problem's constraints, and a genetic algorithm searches for high quality solutions with the minimum possible cost in terms of handoff and cable displayed. We show, by means of computational experiments, the good performance of our approach to this problem.     

一种神经网络参数扰动算法及其在机械手控制中的应用

基于Ｈｏｐｆｉｅｌｄ型网络的收敛特性和Ｋｉｒｐａｔｒｉｃｋ等的模拟退火算法的思想，提出了一种克服Ｈｏｐｆｉｅｌｄ网络的局部极值问题的网络参数扰动算法，它具有类似ＳＡ算法的随机退火的特性。文中通过大量数字模拟分析了该算法的退火性能。     

基于Hopfield神经网络的作业车间生产调度方法

该文提出了基于Hopfield神经网络的作业车间生产调度的新方法.文中给出了作业车 间生产调度问题(JSP)的约束条件及其换位矩阵表示,提出了新的包括所有约束条件的计算能 量函数表达式,得到相应的作业车间调度问题的Hopfield神经网络结构与权值解析表达式,并 提出相应的Hopfield神经网络作业车间调度方法.为了避免Hopfield神经网络容易收敛到局部 极小,从而产生非法调度解的缺点,将模拟退火算法应用于Hopfield神经网络求解,使Hopfield 神经网络收敛到计算能量函数的最小值0,从而保证神经网络输出是一个可行调度方案.该文 改进了已有文献中提出的作业调度问题的Hopfield神经网络方法,与已有算法相比,能够保证 神经网络稳态输出为可行的作业车间调度方案.     

基于暂态混沌神经网在非平衡B指派问题的应用

讨论了非平衡B指派问题的求解算法，给出了暂态混沌神经网络模型，并描述了非平衡B指派问题，提出了基于暂态混沌神经网络的非平衡B指派问题的求解算法。仿真结果表明，该网络可以通过混沌机制来避免陷入局部极小点，从而能够保证快速有效地求解该指派问题。该文还用这种方法求解了属于NP难题的文件分配问题(FAP)。     

Layout optimization of satellite module using soft computing techniques

This paper presents a hybrid method using soft computing techniques to deal with layout design problem of a satellite module. This problem is a three-dimensional layout optimization problem with behavioral constraints, and is difficult to solve in polynomial time. In this study, we firstly used a Hopfield neural network (HNN) to allocate the given apparatuses and equipment to the bearing plate surfaces in the satellite module. Then, we integrated genetic algorithm/particle swarm optimization (GA/PSO) and quasi-principal component analysis (QPCA) to deal with the further detailed layout optimization. The numerical experimental results showed the feasibility and efficiency of our method for layout optimization of a satellite module.     

一种Hopfield神经网络能量函数改进下的作业车间调度*

由于作业车间调度问题的目标函数目前还无法用换位矩阵的元素以数学公式的形式表示,因此无法保证求出全局最优解。文中首先对换位矩阵表示方法进行了改进,给出新的带有目标函数的能量函数表达式,然后提出改进的Hopfield神经网络作业车间调度方法,并将模拟退火应用于Hopfield神经网络求解,避免了陷入局部极值。仿真结果表明,该方法具有全局搜索能力,并能够保证神经网络的稳态输出为全局最优或近似全局最优。     

A Discrete-Time Quantized-State Hopfield Neural Network

A discrete-time quantized-state Hopfield neural network is analyzed with special emphasis in its convergence, complexity and scalability properties. This network can be considered as a generalization of the Hopfield neural network by Shrivastava et al. [27] into the interior of the unit hypercube. This extension allows its use in a larger set of combinatorial optimization problems and its properties make of it a good candidate to build hybrid algorithms along with other heuristics such as the evolutive algorithms. Finally, the network is illustrated in some instances of the linear assignment problem and the frequency assignment problem.     

基于禁忌搜索算法求解随机约束满足问题

为了求解具有增长取值域的随机约束满足问题（CSP）,提出了一种基于禁忌搜索并与模拟退火相结合的算法。首先,利用禁忌搜索得到一组启发式的初始赋值,即由一个随机初始化的可行解通过邻域构造一组候选解,再利用禁忌表使候选解向最小化目标函数值的方向移动;如果得到的最优赋值不是问题的解,就把它作为启发式的初始赋值,再执行模拟退火对这组赋值进行修正直到得到全局最优解。数值实验结果表明,所提算法在接近问题的理论相变阈值时仍然能有效地找到问题的解,与其他局部搜索算法相比,表现出了显著的优越性,可用于随机CSP的算法设计。     

Scheduling multiprocessor job with resource and timing constraintsusing neural networks

The Hopfield neural network is extensively applied to obtaining an optimal/feasible solution in many different applications such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Although providing rapid convergence to the solution, TSP frequently converges to a local minimum. Stochastic simulated annealing is a highly effective means of obtaining an optimal solution capable of preventing the local minimum. This important feature is embedded into a Hopfield neural network to derive a new technique, i.e., mean field annealing. This work applies the Hopfield neural network and the normalized mean field annealing technique, respectively, to resolve a multiprocessor problem (known to be a NP-hard problem) with no process migration, constrained times (execution time and deadline) and limited resources. Simulation results demonstrate that the derived energy function works effectively for this class of problems.     

A new adaptive genetic algorithm for fixed channel assignment

This paper presents a new genetic algorithm (GA) with good convergence properties and a remarkable low computational load. Such features are achieved by on-line tuning up the probabilities of mutation and crossover on the basis of the analysis of the individuals’ fitness entropy. This way, a brand new method to control and adjust the population diversity is obtained. The resulting GA attains quality solutions, thus offering an interesting alternative to other global search techniques, such as simulated annealing, Tabu search and neural networks, as well as to standard GAs. The new algorithm is applied to solve the problem of frequency reuse in mobile cellular communication systems, where the main aim is to obtain a conflict-free channel assignment among the cells such that the resulting bandwidth is close to the minimum channel span required for the whole network. The algorithm performance has been tested and compared by making use of a selection of the most well-known benchmark instances; optimal bandwidth solutions have been achieved within a reasonable computation time.     

Frequency assignment problem in satellite communications using differential evolution

Satellite communications technology has a tremendous impact in refining our world. The frequency assignment problem is of a fundamental importance when it comes to providing high-quality transmissions in satellite communication systems. The NP-complete frequency assignment problem in satellite communications involves the rearrangement of frequencies of one set of carriers while keeping the other set fixed in order to minimize the largest and total interference among carriers. In this paper, we present a number of algorithms, based on differential evolution, to solve the frequency assignment problem. We investigate several schemes ranging from adaptive differential evolution to hybrid algorithms in which heuristic is embedded within differential evolution. The effectiveness and robustness of our proposed algorithms is demonstrated through solving a set of benchmark problems and comparing the results with a number of previously proposed techniques that solve the same problem. Experimental results show that our proposed algorithms, in general, and hybrid ones in particular, outperform the existing algorithms both in terms of the quality of the solutions and computational time.     

A gradual neural-network approach for frequency assignment insatellite communication systems

A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of NxM binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.     

Multi-module satellite component assignment and layout optimization

In previous optimization methods for multi-module satellite equipment (component) layout optimization problem, each component was limited to certain module or supporting surface and could only search its position there. Components could not migrate from one module or supporting surface to another. In this case, the layout design of components within satellite module was seriously hindered from further improvement. In this study a component assignment and layout integration optimization algorithm is presented to deal with this problem, which can assign components to each module of satellite dynamically during optimization procedure. The aim of this paper is to expand the solution space of component layout optimization so as to further improve the component layout design. The proposed component assignment and layout integration optimization algorithm is inspired from the idea of stepwise regression in multiple regression analysis, which allows independent variables to enter or leave regression equation freely. In the proposed algorithm components enter the satellite module one by one in descending order of the product of mass and height. For all supporting surfaces within satellite module, each component will try all of them through layout optimization together with these components have been in the satellite module, and finally select the one with the best fitness as its initial assignment. At the same time, these components have been in the satellite module will be evaluated by their moment of inertia to decide whether they leave the current supporting surfaces and move to another or not. The layout optimization algorithm uses the differential evolution (DE) and random mutation operation to optimize the coordinates and orientations of components, respectively. The performance of the proposed algorithm is finally evaluated on a simplified satellite case. Experimental results show the proposed algorithm outperforms other two algorithms that did not consider component assignment in computational accuracy.