Swarm algorithms with chaotic jumps applied to noisy optimization problems

In this paper, we investigate the use of some well-known versions of particle swarm optimization (PSO): the canonical PSO with gbest model and lbest model with ring topology, the Bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) on noisy optimization problems. As far as we know, some of these versions like BBPSO and FIPS had not been previously applied to noisy functions yet. A hybrid approach which consists of the swarm algorithms combined with a jump strategy has been developed for static environments. Here, we focus on investigating the introduction of the jump strategy to the swarm algorithms now applied to noisy optimization problems. The hybrid approach is compared experimentally on different noisy benchmark functions. Simulation results indicate that the addition of the jump strategy to the swarm algorithms is beneficial in terms of robustness.     

Fault-tolerant communication algorithms in toroidal networks

Fault-tolerant communication algorithms for k-ary n-cubes are introduced. These include: One-to-all broadcasting, all-to-all broadcasting, one-to-all personalized communication, and all-to-all personalized communication. Each of these algorithms can tolerate up to (2n-2) node failures provided that k>(2n-2) and k>3. Extensions of these algorithms with up to 2n-1 node failures are also described. The communication complexities of the proposed algorithms are derived when wormhole or store and forward packet routing is used     

Weight optimisation for iterative distributed model predictive control applied to power networks

This paper presents a weight tuning technique for iterative distributed Model Predictive Control (MPC). Particle Swarm Optimisation (PSO) is used to optimise both the weights associated with disturbance rejection and those associated with achieving consensus between control agents. Unlike centralised MPC, where tuning focuses solely on disturbance rejection performance, iterative distributed MPC practitioners must concern themselves with the trade off between disturbance rejection and the overall communication overhead when tuning weights. This is particularly the case in large scale systems, such as power networks, where typically there will be a large communication overhead associated with control. In this paper a method for simultaneously optimising both the closed loop performance and minimising the communications overhead of iterative distributed MPC systems is proposed. Simulation experiments illustrate the potential of the proposed approach in two different power system scenarios.     

New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems

In the mining optimisation literature, most researchers focused on two strategic-level and tactical-level open-pit mine optimisation problems, which are respectively termed ultimate pit limit (UPIT) or constrained pit limit (CPIT). However, many researchers indicate that the substantial numbers of variables and constraints in real-world instances (e.g., with 50–1000 thousand blocks) make the CPIT's mixed integer programming (MIP) model intractable for use. Thus, it becomes a considerable challenge to solve the large scale CPIT instances without relying on exact MIP optimiser as well as the complicated MIP relaxation/decomposition methods. To take this challenge, two new graph-based algorithms based on network flow graph and conjunctive graph theory are developed by taking advantage of problem properties. The performance of our proposed algorithms is validated by testing recent large scale benchmark UPIT and CPIT instances’ datasets of MineLib in 2013. In comparison to best known results from MineLib, it is shown that the proposed algorithms outperform other CPIT solution approaches existing in the literature. The proposed graph-based algorithms lead to a more competent mine scheduling optimisation expert system because the third-party MIP optimiser is no longer indispensable and random neighbourhood search is not necessary.     

电力通信网络中负载均衡的路由协议

在电力通信网络中,负载均衡能够减少瓶颈节点的过载情况,有助于提升电力通信系统的可靠性和网络资源利用率。针对电力通信网络独特的结构与流量特征,提出一种确定性路由与机会路由相结合的负载均衡的路由协议。每个节点从以自己为中心的区域中选出候选节点集合负责转发数据包,候选节点依据局部的准确代价与远处的估计代价划分优先级并决定转发概率。与负载均衡优先的开放最短路径优先（LBA-OSPF）协议相比,节点平均负载降低了32.3%,端到端时延减少了50.3%。     

Distributed selectsort sorting algorithms on broadcast communication networks

In this paper, a distributed selectsort algorithm and a parameterized selectsort algorithm are presented to be applied on distributed systems for cases when N P where N is the number of elements to be sorted and P is the number of processors in the system. The distributed system considered in this paper uses a broadcasting channel for communication between processors. We show that the number of messages required for the parameterized selectsort algorithm is independent of N and is of complexity O(P), which is optimal in a distributed system with P processors. Furthermore, the amount of communication required in terms of elements is N + O(P³) and the computation time complexity is O((N/P)lgN + P²lg(N/P)). Hence, when N P³, the computation time complexity is O((N/P)lgN), which is optimal using P processors. In addition, this parameterized algorithm provides us with a parameter K such that by choosing the value of K allows us to trade among processing requirement, memory requirement, and communication requirement. It is shown that this parameterized algorithm can reduce the communication requirements significantly while only slightly increasing the computation requirements.     

A review of genetic algorithms applied to training radial basis function networks

The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.     

一种用于农业环境监测的无线传感器网络设计

提出一种应用于农业环境信息监测的无线传感器网络,实现对农业试验田温度、湿度及光照强度的实时监测。处于同一个网络的节点通过点名和轮询机制,实现网络的建立与维护。传感器节点实时地获取所处环境的温度、湿度和光强信息。用户可根据需要查询相应的信息。实验结果表明：该网络能够实现所需信息的获取和传输功能。     

Neural networks applied to a large biological database to analyse dairy breeding patterns

The paper recounts the investigation of a dairy sire prediction capability based on Cascade Correlation neural networks to study influences relating the performance of offspring to their parents. The context of the problem is the artificial insemination breeding program for the Australian dairy industry. The networks are used to screen observed information in the database to relate it to best combinations of dam and sire. The voluminous data is quite noisy and is subject to genetic and environmental influences. The intention is to extract linear and nonlinear relationships from among the input variables without specifying their form. A number of scenarios are employed which recast the data into different forms. In particular, it was discovered that the problem could be restructured and the data supplemented with transformed data to produce succinct input patterns of manageable dimensionality, which allowed for a substantially improved predictive capability. It was then found that reasonable daughter predictions could be obtained of about 10%, as measured by her milk production. Results are compared with those obtained using two alternate neural network methods. Crude statistical methods are employed to evaluate the performance of the neural networks.     

Recursive estimation algorithms for power controls of wireless communication networks

Power control problems for wireless communication networks are investigated in direct-sequence codedivision multiple-access （DS/CDMA） channels. It is shown that the underlying problem can be formulated as a constrained optimization problem in a stochastic framework. For effective solutions to this optimization problem in real time, recursive algorithms of stochastic approximation type are developed that can solve the problem with unknown system components. Under broad conditions, convergence of the algorithms is established by using weak convergence methods.     

Performance analysis of multicast algorithms for mobile satellite communication networks

With the rise of mobile computing and an increasing need for ubiquitous high-speed data connections, Internet-in-the-sky solutions are becoming increasingly viable. To reduce the network overhead of one-to-many transmissions, the multicast protocol has been devised. The implementation of multicast in these low earth orbit (LEO) constellations is a critical component to achieving an omnipresent network environment. This paper examines the system performance associated with two terrestrial-based multicast mobility solutions, distance vector multicast routing protocol (DVMRP) with mobile IP and on demand multicast routing protocol (ODMRP). These protocols are implemented and simulated in a satellite LEO constellation. Results from the simulation trials show the ODMRP protocol provided greater than 99% reliability in packet deliverability, at the cost of more than 8 bits of overhead for every 1 bit of data for multicast groups with multiple sources. In contrast, DVMRP proved robust and scalable, with data-to-overhead ratios increasing logarithmically with membership levels. DVMRP also had less than 70 ms of average end-to-end delay, providing stable transmissions at high loading and membership levels.     

Evolutionary algorithms for continuous-space optimisation

From a global viewpoint, evolutionary algorithms (EAs) working on continuous search-spaces can be regarded as homogeneous Markov chains (MCs) with discrete time and continuous state. We analyse from this viewpoint the (1?+?1)EA on the inclined plane fitness landscape, and derive a closed-form expression for the probability of occupancy of an arbitrary target zone, at an arbitrary iteration of the EA. For the hitting-time of an arbitrary target zone, we provide lower and upper bounds, as well as an asymptotic limit. Discretization leads to an MC with discrete time, whose simple structure is exploited to carry out efficient numerical investigations of the theoretical results obtained. The numerical results thoroughly confirm the theoretical ones, and also suggest various conjectures which go beyond the theory.     

Adaptive feedback control algorithms for large data transfers inhigh-speed networks

Two linear feedback control algorithms for handling and preventing congestion in broadband asynchronous transfer mode networks are proposed and analyzed. The fluid approximation model is described with a continuous-time system of delay-differential equations. The algorithms are asymptotically stable, and the transient processes are nonoscillatory. The control parameters are locally optimal (optimality is based on the asymptotic rate of convergence). The results of numerical experiments suggest that these parameters are globally optimal as well     

Optimal switch location in mobile communication networks using hybrid genetic algorithms

The optimal positioning of switches in a mobile communication network is an important task, which can save costs and improve the performance of the network. In this paper we propose a model for establishing which are the best nodes of the network for allocating the available switches, and several hybrid genetic algorithms to solve the problem. The proposed model is based on the so-called capacitated p-median problem, which have been previously tackled in the literature. This problem can be split in two subproblems: the selection of the best set of switches, and a terminal assignment problem to evaluate each selection of switches. The hybrid genetic algorithms for solving the problem are formed by a conventional genetic algorithm, with a restricted search, and several local search heuristics. In this work we also develop novel heuristics for solving the terminal assignment problem in a fast and accurate way. Finally, we show that our novel approaches, hybridized with the genetic algorithm, outperform existing algorithms in the literature for the p-median problem.     

Randomized algorithms for stability and robustness analysis of high-speed communication networks

This paper initiates a study toward developing and applying randomized algorithms for stability of high-speed communication networks. The focus is on congestion and delay-based flow controllers for sources, which are "utility maximizers" for individual users. First, we introduce a nonlinear algorithm for such source flow controllers, which uses as feedback aggregate congestion and delay information from bottleneck nodes of the network, and depends on a number of parameters, among which are link capacities, user preference for utility, and pricing. We then linearize this nonlinear model around its unique equilibrium point and perform a robustness analysis for a special symmetric case with a single bottleneck node. The "symmetry" here captures the scenario when certain utility and pricing parameters are the same across all active users, for which we derive closed-form necessary and sufficient conditions for stability and robustness under parameter variations. In addition, the ranges of values for the utility and pricing parameters for which stability is guaranteed are computed exactly. These results also admit counterparts for the case when the pricing parameters vary across users, but the utility parameter values are still the same. In the general nonsymmetric case, when closed-form derivation is not possible, we construct specific randomized algorithms which provide a probabilistic estimate of the local stability of the network. In particular, we use Monte Carlo as well as quasi-Monte Carlo techniques for the linearized model. The results obtained provide a complete analysis of congestion control algorithms for internet style networks with a single bottleneck node as well as for networks with general random topologies.     

Efficient architectures and algorithms for multicasting data in computer communication networks

This paper presents two network architectures with associated routing and multicast algorithms for improved performance under multicasting traffic conditions. A conditionally nonblocking network, referred to as a Clos network, forms the basis for the development of efficient multicast communication networks. The Clos network is first analyzed under multicast traffic conditions for blocking and multicast overflow probability. The analysis determines the overflow probability under two different multicast distribution assumptions. The first distribution assumes all packets request the same number of copies and the second distribution uses a random number of requested copies. An analysis of an extension of the presented network to multiplexed parallel planes of a network shows a significant improvement on the network performance and particularly on the carried traffic load when compared with previously published multicast architectures using different buffering strategies.     

Standard clock simulation and ordinal optimization applied to admission control in integrated communication networks

In this paper we apply the ideas of ordinal optimization and the technique of Standard Clock (SC) simulation to the voice-call admission-control problem in integrated voice/data multihop radio networks. This is an important problem in networking that is not amenable to exact analysis by means of the usual network modeling techniques. We first describe the use of the SC approach on sequential machines, and quantify the speedup in simulation time that is achieved by its use in a number of queueing examples. We then develop an efficient simulation model for wireless integrated networks based on the use of the SC approach, which permits the parallel simulation of a large number of admission-control policies, thereby reducing computation time significantly. This model is an extension of the basic SC approach in that it incorporates fixed-length data packets, whereas SC simulation is normally limited to systems with exponentially distributed interevent times. Using this model, we demonstrate the effectiveness of ordinal-optimization techniques, which provide a remarkably good ranking of admission-control policies after relatively short simulation runs, thereby facilitating the rapid determination of good policies. Moreover, we demonstrate that the use of crude, inaccurate analytical and simulation models can provide highly accurate policy rankings that can be used in conjunction with ordinal-optimization methods, provided that they incorporate the key aspects of system operation.     

Analysis and design of AIMD congestion control algorithms in communication networks

We present a model of a network of synchronised sources operating additive increase multiplicative decrease (AIMD) congestion control algorithms. We show: (i) that networks of such devices in the presence of a drop-tail bottleneck buffer may be modelled as a positive linear system; (ii) that such networks possess a unique stationary point; and (iii) that this stationary point is globally exponentially stable. We use these results to establish conditions for the fair co-existence of traffic in networks employing heterogeneous AIMD algorithms and to design a new protocol for operation over high-speed and long-distance links.     

Local search optimisation applied to the minimum distance problem

In practical terms all coded electronic signals are prone to corruption during transmission but may be corrected by using error-correcting codes. The minimum distance of a code is important because it is the major parameter affecting the error-correcting performance of a code. In this paper a recent heuristic combinatorial optimisation algorithm, called ant colony optimisation (ACO), is applied to the problem of determining minimum distances of error-correcting codes.The ACO algorithm is motivated by analogy with natural phenomena, in particular, the ability of a colony of ants to ‘optimise’ their collective endeavours. In this paper the biological background for ACO is explained and its computational implementation is presented in an error-correcting code context. The particular implementation of ACO makes use of a tabu search (TS) improvement phase to give a computationally enhanced algorithm (ACOTS). Two classes of codes are then used to show that ACOTS is a useful and viable optimisation technique to investigate minimum distances of error-correcting codes.     

Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains

Nowadays, large scale optimisation problems arise as a very interesting field of research, because they appear in many real-world problems (bio-computing, data mining, etc.). Thus, scalability becomes an essential requirement for modern optimisation algorithms. In a previous work, we presented memetic algorithms based on local search chains. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration reached by this one. Using this technique, it was presented a memetic algorithm, MA-CMA-Chains, using the CMA-ES algorithm as its local search component. This proposal obtained very good results for continuous optimisation problems, in particular with medium-size (with up to dimension 50). Unfortunately, CMA-ES scalability is restricted by several costly operations, thus MA-CMA-Chains could not be successfully applied to large scale problems. In this article we study the scalability of memetic algorithms based on local search chains, creating memetic algorithms with different local search methods and comparing them, considering both the error values and the processing cost. We also propose a variation of Solis Wets method, that we call Subgrouping Solis Wets algorithm. This local search method explores, at each step of the algorithm, only a random subset of the variables. This subset changes after a certain number of evaluations. Finally, we propose a new memetic algorithm based on local search chains for high dimensionality, MA-SSW-Chains, using the Subgrouping Solis Wets’ algorithm as its local search method. This algorithm is compared with MA-CMA-Chains and different reference algorithms, and it is shown that the proposal is fairly scalable and it is statistically very competitive for high-dimensional problems.