Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algorithm with A Seeded Population

Feb 25, 20072 likes636 views

A recent study Sastry and Xiao (2001) proposed a highly reliable cluster optimization algorithm which employed extended compact genetic algorithm (ECGA) along with Nelder-Mead simplex search. This study utilizes an efficiency enhancement technique for the ECGA based cluster optimizer to reduce the population size and the number of function evaluation requirements, yet retaining the high reliability of predicting the lowest energy structure. Seeding of initial population with lowest energy structures of smaller cluster has been employed as the efficiency enhancement technique. Empirical results indicate that the population size and total number of function evaluations scale up with the cluster size are reduced from O(n4.2) and O(n8.2) to O(n0.83) and O(n2.45) respectively.

Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algorithm with A Seeded Population

Most real life networks are hybrid networks. A hybrid network combines the best features of two or more networks. In networking terminology, a hybrid network according to “Information Technology control and Audit” are reliable and versatile. They provide large numbers of connections and data transmission paths to users. These hybrid networks may be treated as network nodes for analysis purpose. The two radio access technologies (RATs) are considered with cellular/WLAN depending on whether it is voice/ data, video. For observing practical result, some setups are required in laboratory which includes interface from personal computer to mobile terminal and vice-versa. The network simulator (Ns-2) is used for analysis purpose. The results shows the parameters like energy good put, Bandwidth utilization, packet delivery ratio, energy good put and energy consumed are improved after using hybrid networks.

Cluster Head Selection Techniques for Energy Efficient Wireless Sensor Networ...ijsrd.com

Wireless sensor networks are widely considered as one of the most important technologies. The Wireless Sensor Network (WSN) is a wireless network consisting of ten to thousand small nodes with sensing, computing and wireless communication capabilities. They have been applied to numerous fields such as healthcare, monitoring system, military, and so forth. Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Energy efficiency is thus a primary issue in maintaining the network. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks. This paper elaborates several techniques like LEACH, HEED, LEACH-B, PEACH, EEUC of cluster head selection for energy efficient in wireless sensor networks.

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The document summarizes work done on improving the LEACH routing protocol for wireless sensor networks. It provides background on WSNs and discusses limitations of the original LEACH protocol. The work done section describes an algorithm that tracks "perfect clusters" based on distances between a cluster head and nodes, and adjusts node thresholds to promote repetition of such clusters while limiting energy depletion. Simulation conditions consider 100 homogeneous nodes randomly placed to transmit data to a base station.

A New Method for Reducing Energy Consumption in Wireless Sensor Networks usin...Editor IJCATR

Nowadays, wireless sensor networks, clustering protocol based on the neighboring nodes into separate clusters and fault tolerance for each cluster exists for sensors to send information to the base station, to gain the best performance in terms of increased longevity and maintain tolerance than with other routing methods. However, most clustering protocols proposed so far, only geographical proximity (neighboring) cluster formation is considered as a parameter. In this study, a new clustering protocol and fault tolerance based on the fuzzy algorithms are able to clustering nodes in sensor networks based on fuzzy logic and fault tolerance. This protocol uses clustering sensor nodes and fault tolerance exist in the network to reduce energy consumption, so that faulty sensors from neighboring nodes are used to cover the errors, work based on the most criteria overlay neighbor sensors with defective sensors, distance neighbor sensors from fault sensor and distance neighbor sensors from central station is done. Superior performance of the protocol can be seen in terms of increasing the network lifetime and maintain the best network tolerance in comparison with previous protocols such as LEACH in the simulation results.

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Mobile communication devices have become popular tools for gathering and disseminating information and data. When sensitive information is exchanged using SMS, it is crucial to protect the content from eavesdroppers as well as ensuring that the message is sent by a legitimate sender. Using an encryption technique to secure SMS data increases its length and accordingly the cost of sending it. This paper provides a hybrid compression encryption technique to secure the SMS data. The proposed technique compresses the SMS to reduce its length, then encrypts it using RSA algorithm. A signature is added to the encrypted SMS for signing it to differentiate it from other SMS messages in SMSINBOX. The experimental results which are based on Symbian OS show that the proposed technique guarantees SMS data security without increasing its size.

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Progressive Routing Protocol using Hybrid Analysis for MANETsidescitation

In this paper, we proposed a replacement hybrid multipath routing protocol for MANET known as Hybrid Multipath Progressive Routing Protocol for MANET (HMPRP), during this work we improve the performance of accepted MANET routing protocols, namely, the Ad-hoc On-demand Distance Vector routing protocol and use of their most popular properties to formulate a replacement Hybrid routing protocol using the received signal strength. The proposed routing protocol optimizes the information measure usage of MANETs by reducing the routing overload and overhead. This proposed routing protocol additionally extends the battery lifetime of the mobile devices by reducing the specified variety of operations for (i) Route determination (ii) for packet forwarding. Simulation results are used to draw conclusions regarding the proposed routing algorithm and compared it with the AODV, OLSR, and ZRP protocol. Experiments carried out based on this proposed algorithm, shows that better performance are achieved with regard to AODV, OLSR, and ZRP routing algorithm in terms of packet delivery ratio, throughput, energy consumed and end-to-end packet delay.

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Genetic Algorithms and Genetic Programming for Multiscale Modelingkknsastry

Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately.

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The document describes research into using efficient estimation of distribution algorithms (EDAs) like the compact genetic algorithm (cGA) to solve optimization problems involving billions of variables. Key aspects discussed include the cGA's memory and computational efficiency through techniques like parallelization and vectorization. The researchers were able to solve a noisy OneMax problem involving over 33 million variables to optimality and a problem with 1.1 billion variables with relaxed convergence. The document argues this research is important because many real-world problems involving nanotechnology, biology, and information systems require solving optimization problems at massive scales.

Empirical Analysis of ideal recombination on random decomposable problemskknsastry

This paper analyzes the behavior of a selectorecombinative genetic algorithm (GA) with an ideal crossover on a class of random additively decomposable problems (rADPs). Specifically, additively decomposable problems of order k whose subsolution fitnesses are sampled from the standard uniform distribution U[0,1] are analyzed. The scalability of the selectorecombinative GA is investigated for 10,000 rADP instances. The validity of facetwise models in bounding the population size, run duration, and the number of function evaluations required to successfully solve the problems is also verified. Finally, rADP instances that are easiest and most difficult are also investigated.

Population sizing for entropy-based model buliding In genetic algorithms kknsastry

This paper presents a population-sizing model for the entropy-based model building in genetic algorithms. Specifically, the population size required for building an accurate model is investigated. The effect of the selection pressure on population sizing is also incorporated. The proposed model indicates that the population size required for building an accurate model scales as Θ(m log m), where m is the number of substructures and proportional to the problem size. Experiments are conducted to verify the derivations, and the results agree with the proposed model.

Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry

This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(l logl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(l logl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(l).

Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry

In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.

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