Darwinian particle swarm optimization

Particle Swarm Optimization (PSO), an evolutionary algorithm for optimization is extended to determine if natural selection, or survival-of-the- fittest, can enhance the ability of the PSO algorithm to escape from local optima. To simulate selection, many simultaneous, parallel PSO algorithms, each one a swarm, operate on a test problem. Simple rules are developed to implement selection. The ability of this so-called Darwinian PSO to escape local optima is evaluated by comparing a single swarm and a similar set of swarms, differing primarily in the absence of the selection mechanism, operating on the same test problem. The selection process is shown to be capable of evolving the best type of particle velocity control, which is a problem specific design choice of the PSO algorithm

Particle Swarm Optimization for the Clustering of Wireless Sensors

Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a \u27swarm\u27 of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network

A Distributed Evolutionary Algorithmic Approach to the Coverage Problem for Submersible Sensors

Untethered, underwater sensors, deployed for event detection and tracking and operating in an autonomous mode will be required to self-assemble into a configuration, which optimizes their coverage, effectively minimizing the probability that an event in the target area goes undetected. This organized, cooperative, and autonomous, spreading-out of the sensors is complicated due to sensors localized communication. A given sensor will not in general have position and velocity information for all sensors, but only for those in its communication area. A possible approach to this problem, motivated by an evolutionary optimization technique, Particle Swarm Optimization (PSO) is proposed and extended in a novel way. A distributed version of PSO is developed. A distributed version of PSO is explored using experimental fitness to address the coverage problem in a two dimensional area

A Distributed Evolutionary Algorithmic Approach to the Least-Cost Connected Constrained Sub-Graph and Power Control Problem

When wireless sensors are capable of variable transmit power and are battery powered, it is important to select the appropriate transmit power level for the node. Lowering the transmit power of the sensor nodes imposes a natural clustering on the network and has been shown to improve throughput of the network. However, a common transmit power level is not appropriate for inhomogeneous networks. A possible fitness-based approach, motivated by an evolutionary optimization technique, Particle Swarm Optimization (PSO) is proposed and extended in a novel way to determine the appropriate transmit power of each sensor node. A distributed version of PSO is developed and explored using experimental fitness to achieve an approximation of least-cost connectivity

An Evolutionary Algorithmic Approach to Learning a Bayesian Network from Complete Data

Discovering relationships between variables is crucial for interpreting data from large databases. Relationships between variables can be modeled using a Bayesian network. The challenge of learning a Bayesian network from a complete dataset grows exponentially with the number of variables in the database and the number of states in each variable. It therefore becomes important to identify promising heuristics for exploring the space of possible networks. This paper utilizes an evolutionary algorithmic approach, Particle Swarm Optimization (PSO) to perform this search. A fundamental problem with a search for a Bayesian network is that of handling cyclic networks, which are not allowed. This paper explores the PSO approach, handling cyclic networks in two different ways. Results of network extraction for the well-studied ALARM network are presented for PSO simulations where cycles are broken heuristically at each step of the optimization and where networks with cycles are allowed to exist as candidate solutions, but are assigned a poor fitness. The results of the two approaches are compared and it is found that allowing cyclic networks to exist in the particle swarm of candidate solutions can dramatically reduce the number of objective function evaluations required to converge to a target fitness value

Deep Transductive Transfer Learning for Automatic Target Recognition

One of the major obstacles in designing an automatic target recognition (ATR) algorithm, is that there are often labeled images in one domain (i.e., infrared source domain) but no annotated images in the other target domains (i.e., visible, SAR, LIDAR). Therefore, automatically annotating these images is essential to build a robust classifier in the target domain based on the labeled images of the source domain. Transductive transfer learning is an effective way to adapt a network to a new target domain by utilizing a pretrained ATR network in the source domain. We propose an unpaired transductive transfer learning framework where a CycleGAN model and a well-trained ATR classifier in the source domain are used to construct an ATR classifier in the target domain without having any labeled data in the target domain. We employ a CycleGAN model to transfer the mid-wave infrared (MWIR) images to visible (VIS) domain images (or visible to MWIR domain). To train the transductive CycleGAN, we optimize a cost function consisting of the adversarial, identity, cycle-consistency, and categorical cross-entropy loss for both the source and target classifiers. In this paper, we perform a detailed experimental analysis on the challenging DSIAC ATR dataset. The dataset consists of ten classes of vehicles at different poses and distances ranging from 1-5 kilometers on both the MWIR and VIS domains. In our experiment, we assume that the images in the VIS domain are the unlabeled target dataset. We first detect and crop the vehicles from the raw images and then project them into a common distance of 2 kilometers. Our proposed transductive CycleGAN achieves 71.56% accuracy in classifying the visible domain vehicles in the DSIAC ATR dataset.Comment: 10 pages, 5 figure

Effect of Arthrospira platensis against sodium fluoride-induced haematological alterations

455-459The objective of present study was to investigate the effects of hydroalcoholic extract of Arthrospira platensis (ASP) against sodium fluoride (NaF) induced hematological alterations. Thirty-six male Wistar albino rats were divided into six groups of six animals each. Group I served as normal control. Group II served as toxic control. Group III served as plant control received ASP at a dose of 400 mg/kg body weight (p.o). Groups IV-VI served as treatment groups, which received the hydro alcoholic extract of ASP at doses of 100, 200 and 400 mg per kg body weight (p.o), respectively. All except group I and III received NaF (100 ppm) through drinking water for 30 days. Various blood parameters such as leukocyte count, erythrocyte count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration and platelet count were estimated. Results showed that ASP restored fluoride-induced hematological changes. In conclusion, the present study revealed that ASP has the good mitigative effect against sodium fluoride-induced hematological changes

Effect of Arthrospira platensis against sodium fluoride-induced haematological alterations

455-459The objective of present study was to investigate the effects of hydroalcoholic extract of Arthrospira platensis (ASP) against sodium fluoride (NaF) induced hematological alterations. Thirty-six male Wistar albino rats were divided into six groups of six animals each. Group I served as normal control. Group II served as toxic control. Group III served as plant control received ASP at a dose of 400 mg/kg body weight (p.o). Groups IV-VI served as treatment groups, which received the hydro alcoholic extract of ASP at doses of 100, 200 and 400 mg per kg body weight (p.o), respectively. All except group I and III received NaF (100 ppm) through drinking water for 30 days. Various blood parameters such as leukocyte count, erythrocyte count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration and platelet count were estimated. Results showed that ASP restored fluoride-induced hematological changes. In conclusion, the present study revealed that ASP has the good mitigative effect against sodium fluoride-induced hematological changes

Effect of Arthrospira platensis against Sodium fluoride induced haematological alterations

The objective of present study was to investigate the effects of hydroalcoholic extract of Arthrospira platensis (ASP) against sodium fluoride (NaF) induced hematological alterations. Thirty-six male Wistar albino rats were divided into six groups of six animals each. Group I served as normal control. Group II served as toxic control. Group III served as plant control received ASP at a dose of 400 mg/kg body weight (p.o). Groups IV-VI served as treatment groups, which received the hydro alcoholic extract of ASP at doses of 100, 200 and 400 mg per kg body weight (p.o), respectively. All except group I and III received NaF (100 ppm) through drinking water for 30 days. Various blood parameters such as leukocyte count, erythrocyte count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration and platelet count were estimated. Results showed that ASP restored fluoride induced hematological changes. In conclusion, the present study revealed that ASP has the good mitigative effect against sodium fluoride-induced hematological changes

Optimal Topologies for Wireless Sensor Networks

Since untethered sensor nodes operate on battery, and because they must communicate through a multi-hop network, it is vital to optimally configure the transmit power of the nodes both to conserve power and optimize spatial reuse of a shared channel. Current topology control algorithms try to minimize radio power while ensuring connectivity of the network. We propose that another important metric for a sensor network topology will involve consideration of hidden nodes and asymmetric links. Minimizing the number of hidden nodes and asymmetric links at the expense of increasing the transmit power of a subset of the nodes may in fact increase the longevity of the sensor network. In this paper we explore a distributed evolutionary approach to optimizing this new metric. Inspiration from the Particle Swarm Optimization technique motivates a distributed version of the algorithm. We generate topologies with fewer hidden nodes and asymmetric links than a comparable algorithm and present some results that indicate that our topologies deliver more data and last longer