A genetic algorithm for the multiple destination routing problems

A scheme for enhancing sensor network lifetime is proposed using multiple mobile CHs. The CHs controllably move toward the energy-rich sensors maintaining remaining energy more evenly, eliminating multihop transmission. Collaboration among CHs is considered to maintain their continuous connectivity to the base station (BS) to reduce the delay in communication. Network lifetime is increased by 75% by making CHs always move towards a stable equilibrium point whereas connectivity algorithm provides 40% improvement in transmission delays over existing schemes.

A heuristic algorithm based on Monte Carlo methods for the Rural Postman Problem

The emergence of nomadic multimedia applications, such as multimedia conferencing, distance learning, video phones, video/movie-on-demand, and education-on-demand, has recently generated much interest in multi-hop wireless mesh networks (WMNs) to support diverse Quality-of-Service (QoS). In the existing WMN QoS routing protocols, the methods of bandwidth calculation and allocation were proposed to determine routes with bandwidth guaranteed for QoS applications. This paper studies two NP-hard problems, the maximum bandwidth routing problem (abbreviated to MBRP) and the maximum flow routing problem (abbreviated to MFRP). Given a source node s and a destination node d in a multi-hop wireless mesh network, the MBRP is to determine an s-to-d path that can carry a maximum amount of traffic from s to d and the MFRP is to determine the maximum flow from s to d, both retaining the network bandwidth-satisfied. In this paper, heuristic algorithms for the two problems are proposed. Upper bounds on their optimal values are derived, and a lower bound is derived on the feasible value obtained for the MBRP. With the upper bound and the lower bound, an approximation ratio for the heuristic algorithm of the MBRP is obtained. The effectiveness of the heuristic algorithms is further verified by experiments. A generalized interference model is also discussed.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Multistage interconnection networks (MINs) are popular in switching and communication applications and have been used in telecommunication and parallel computing systems for many years. Crosstalk a major problem introduced by an optical MIN, is caused by coupling two signals within a switching element. We focus on an efficient solution to avoiding crosstalk by routing traffic through an N X N optical network to avoid coupling two signals within each switching element using wavelength-division multiplexing (WDM) and a time-division approach. Under the constraint of avoiding crosstalk, the interest is on realizing a permutation that uses the minimum number of passes for routing. This routing problem is an NP-hard problem. Many heuristic algorithms are already designed by researchers to perform this routing such as a sequential algorithm, a degree-descending algorithm, etc. The genetic algorithm is used successfully to improve the performance over the heuristic algorithms. The drawback of the genetic algorithm is its long running times. We use the simulated annealing algorithm to improve the performance of solving the problem and optimizing the result. In addition, a wavelength lower bound estimate on the minimum number of passes required is calculated and compared to the results obtained using heuristic, genetic, and simulated annealing algorithms. Many cases are tested and the results are compared to the results of other algorithms to show the advantages of simulated annealing algorithm.

In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and sequence-dependent setup time in realistic situations is presented. Since few works have been implemented in this field, the necessity of finding better solutions is a motivation to extend heuristic or meta-heuristic algorithms. This type of production system is found in industries such as food processing, chemical, textile, metallurgical, printed circuit board, and automobile manufacturing. A mixed integer linear programming (MILP) model is proposed to minimize the makespan. Since this problem is known as NP-Hard class, a meta-heuristic algorithm, named Genetic Algorithm (GA), and three heuristic algorithms (Johnson, SPTCH and Palmer) are proposed. Numerical experiments of different sizes are implemented to evaluate the performance of presented mathematical programming model and the designed GA in compare to heuristic algorithms and a benchmark algorithm. Computational results indicate that the designed GA can produce near optimal solutions in a short computational time for different size problems.

Genetic Algorithms (GAs) have been applied in many complex combinatorial optimization problems and have been proven to yield reasonably good solutions due to their ability of searching in continuous spaces and avoiding local optima. However, one issue in GA application that needs to be carefully explored is to examine sensitivity of critical parameters that may affect the quality of solutions. The key critical GA parameters affecting solution quality include the number of genetic operators, the number of encoded decision variables, the parameter for selective pressure, and the parameter for non-uniform mutation. The effect of these parameters on solution quality is particularly significant for complex problems of combinatorial nature. In this paper the authors test the sensitivity of critical GA parameters in optimizing 3-dimensional highway alignments which has been proven to be a complex combinatorial optimization problem for which an exact solution is not possible warranting the application of heuristics procedures, such as GAs. If GAs are applied properly, similar optimal solutions should be expected at each replication. The authors perform several example studies in order to arrive at a general set of conclusions regarding the sensitivity of critical GA parameters on solution quality. The first study shows that the optimal solutions obtained for a range of scenarios consisting of different combinations of the critical parameters are quite close. The second study shows that different optimal solutions are obtained when the number of encoded decision variables is changed.

SummaryNowadays, research based on mobile ad hoc network's (MANET) performance improvement has drawn a particular concentration from scientists worldwide. The shortest path selection among destination and source nodes determines the network's throughput in MANET. In addition, power management and energy consumption are also the major difficulties that MANET suffers. The conventional routing protocols are intended for static bonding, but that protocol does not satisfy the stability in ad hoc networks by means of regularly changing connectivity. This shows the routing problem in MANETs. Therefore, to overcome the routing issues and maintain energy efficiently, the Type‐2 fuzzy‐based Starling Murmuration Optimizer (SMO)‐War Strategy Optimization (WSO) (T2FSMO‐WSO) routing protocol is proposed in this paper. MANET takes into account four input parameters: route length, active link ratio to global traffic, network lifetime, and control packet ratio. The SMO algorithm is employed to choose the relay node with low energy consumption. The trapezoidal membership functions are used to describe the fuzzy sets for these parameters. The content‐based routing among source and destination nodes is determined using the WSO algorithm. The simulation outcomes reveal that the proposed method discovers highly stable paths with minimum distance and minimized the consumption of energy.

We address the problem of routing mobile agent with fusion cost in WSN under two scenarios: all source nodes, and a part of source nodes to be visited, respectively. Two objective functions in terms of minimizing transmission and fusion energy expenditure in the two scenarios are formulated, and proved to be NP-complete. Furthermore, we propose a two-level encoding based genetic algorithm (GA) to compute a suboptimal solution for the first scenario, and for the second scenario, we propose two computation paradigms to obtain a suboptimum: three-level encoding based GA, and two-step GA which consists of a two-level encoding GA and a simple GA. Simulation results show that proposed GA outperforms local closest first method (LCF) with different unit fusion costs and data reduction ratios in the first scenario. In the second scenario, simulation results with different conditions show that proposed algorithms are feasible, and three-level encoding based GA outperforms two-step GA in solution quality but the latter has superior performance in convergence speed.

A multiple-input multiple-output (MIMO) relay network is composed of source, relay, and destination nodes, each of which is equipped with multiple antennas. Network information theory has shown that the use of multiple relay nodes in source and destination (S-D) communication makes the capacity of the S-D system logarithmically increase with the number of relay nodes. In [13], we proposed a MIMO relay scheme for a relay network with single source and destination pair; each of the multiple relay nodes performs QR decomposition of backward and forward channels in conjunction with phase control (QR-P-QR). In this paper, we extend this scheme to the MIMO relay network with multiple source and destination pairs. For this aim, we use the group nulling approach to decompose the multiple S-D MIMO relay channel to parallel independent S-D MIMO relay channels, and apply the QR-P-QR scheme in each of the decomposed MIMO relay links. Numerical examples show that the proposed relay scheme offers higher capacity than the existing zero-forcing (ZF) relaying scheme

Genetic algorithms have demonstrated considerable success in providing good solutions to many NP-hard optimization problems. For such problems, exact algorithms that always find an optimal solution are only useful for small toy problems, so heuristic algorithms such as the genetic algorithm must be used in practice. In this paper, we apply the genetic algorithm to the NP-hard problem of multiple fault diagnosis (MFD). We compare a pure genetic algorithm with several variants that include local improvement operators. These operators, which are often domain-specific, are used to accelerate the genetic algorithm in converging on optimal solutions. Our empirical results indicate that by using the appropriate local improvement operator, the genetic algorithm is able to find an optimal solution in all but a tiny fraction of the cases and at a speed orders of magnitude faster than exact algorithms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We address the multicast problem in a wireless multihop network in the presence of errors. Finding the multicast schedule that minimizes the expected number of transmissions, while accounting for both the wireless broadcast advantage (WBA) and the wireless unreliable transmission (WUT) characteristics, has not been solved optimally before. We formally define the problem and propose an initial algorithm of non-polynomial complexity to obtain the optimal solution. The algorithm starts by transforming the wireless network topology into an auxiliary expanded graph that captures the WBA property, and assigning appropriate weights to the links so as to capture the WUT property. The proposed transformation is interesting on its own merit, since it permits us to consider only point-to-point transmissions (as in wireline networks) in the expanded graph, instead of the point-to-multipoint transmissions present in the original wireless network, and can also be useful in other optimization problems some of which we briefly describe. By solving the minimum Steiner tree problem on the expanded graph we obtain the optimal multicast solution for the initial graph. Since the optimal algorithm is of non-polynomial complexity, we also propose a number of heuristic algorithms. In particular, we first present a truncated graph transformation and then describe two heuristic algorithms for obtaining the multicast schedule. Simulation results show that the proposed heuristics have performance close to that of the optimal algorithm, at least for the instances for which we were able to track optimal solutions, while outperforming other heuristic multicast algorithms.

A genetic algorithm is presented to find the arrival probability in a directed acyclic network with stochastic parameters, that gives more reliability of transmission flow in delay sensitive networks. Some sub-networks are extracted from the original network, and a connection is established between the original source node and the original destination node by randomly selecting some local source and the local destination nodes. The connections are sorted according to their arrival probabilities and the best established connection is determined with the maximum arrival probability. There is an established discrete time Markov chain in the network. The arrival probability to a given destination node from a given source node in the network is defined as the multi-step transition probability of the absorbtion in the final state of the established Markov chain. The proposed method is applicable on large stochastic networks, where the previous methods were not. The effectiveness of the proposed method is illustrated by some numerical results with perfect fitness values of the proposed genetic algorithm.

Recent research has shown that the cooperative diversity can efficiently combat the fading effects caused by the hostile wireless channels just as the multiple-input multiple-output (MIMO) antenna systems do. Besides, due to the inherent cost and size constraints by many small area wireless networks, e.g. wireless ad hoc networks and wireless sensor networks, in such environments, it would be almost impossible to deploy the antenna array in a mobile node. However, cooperative diversity can provide an economical and viable solution by sharing each mobile node’s single-antenna with others to form a virtual antenna array, while it still can maintain significant performance gains similar to MIMO systems. Meanwhile, the adaptive modulation is a proven technique which can significantly increase the spectral efficiency when perfect channel estimation can be achieved, and many wireless standards have adopted it as an implementation option. It also has been shown that combining these two techniques can greatly raise the system efficiency. Nevertheless, in the multiple nodes cooperative communication networks, the randomness of demodulation process at each relay node makes the task of correctly selecting the forwarding relay nodes very complicated. In view of this problem, a novel adaptive node selection algorithm is proposed for the destination node to effectively select the forwarding relay nodes to help the source node transfer the signals. In this paper, the decode-and-forward (DF) protocol is employed as the cooperative diversity signaling, and the constant-power variable-rate M-ary phase shift keying (M-PSK) is adopted as the adaptive modulation scheme. At the destination node, the maximal-ratio-combining (MRC) method is used to combine all of the signals from diversity branches with the main path transmitted directly from the source node. Because of the random phenomenon of the demodulation process, the outage requirement is used as the baseline in the first stage of the proposed node selection algorithm to choose the maximal transmission rate. Hence the maximal overall system throughput in terms of rate can be achieved. In the meantime, the first candidate forwarding sets of the relay nodes which collectively meet the required outage probability are also been chosen. Then in the second stage, we can further refine the number of the relay nodes among the first candidate forwarding sets by using the following criterion — maximum average rate per node. It will screen out the first candidate sets with the minimum number of the relay nodes. If only one candidate set is left at the end of the second stage, it will be the end of the proposed algorithm. However, if the result of the screening still has more than one set, then the final election among the second candidate sets would be the one which can give the minimum outage probability. In the simulation results, our proposed node selection algorithm can even further improve the spectral efficiency while the deleteriously fading effect induced by the wireless channels can also be effectively removed.

This paper proposes a discrete particle swarm optimization (DPSO) to solve the multiple destination routing (MDR) problems. The problem has been proven to be NP-complete and the traditional heuristics (e.g., the SPH, DNH and ADH) are inefficient in solving it. The particle swarm optimization (PSO) is an efficient global search algorithm and is promising in dealing with complex problems. This paper extends the PSO to a discrete PSO and uses the DPSO to solve the MDR problem. The global search ability and fast convergence ability of the DPSO make it efficient to the problem. Experiments based on the benchmarks from the OR-library show that the DPSO obtains better results when compared with traditional heuristic algorithms, and also outperforms the GA-based algorithm with faster convergence speed.KeywordsParticle swarm optimization (PSO)multiple destination routing problemsSteiner tree problem