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 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

Making water services last: a community-based management model for service sustainability in Ethiopia

It has been known for many years that efforts to professionalise community management and establish some of the necessary support functions required to keep services running can help to improve service levels and ultimately the longevity of services. This paper outlines WaterAid’s experience in Ethiopia of establishing management arrangements for multi-village piped schemes. Since the early 1990s, WaterAid has established a number of Rural Water Boards which remain operational today and ensure water supply services reach hundreds of thousands of people. Their relevance remains as strong today as the Government of Ethiopia seeks to establish more multi-village piped schemes as a means of providing greater water security to rural populations. This paper presents the Ticho multi-village water supply scheme to highlight positive lessons and some of the remaining challenges in professionalising community management

Preserving Bibliographic Relationships in Mappings from FRBR to BIBFRAME 2.0

In the environment of the World Wide Web large volumes of library data have been pub-lished following different conceptual models. The navigation through these volumes and the data interlinking require the development of mappings between the conceptual models. Library conceptual models provide constructs for the representation of bibliographic families and the relationships between Works. A key requirement for successful map-pings between different conceptual models is to preserve such content relationships. This paper studies a set of cases (Work with single Expression, Work with multiple Expres-sions, translation, adaptation) to examine if and how bibliographic content relationships and families could be preserved in mappings from FRBR to BIBFRAME 2.0. Even though, relationships between Works of the same bibliographic family may be preserved, the progenitor Work is not always represented in BIBFRAME after mappings

The comparative performance of three screening questionnaires for psoriatic arthritis in a primary care surveillance study

Objectives: The objective of this study was to compare the performance of three PsA screening questionnaires in a primary care psoriasis surveillance study. Methods: Participants with psoriasis, and not known to have PsA, were identified from general practice databases and invited to attend a secondary care centre for a clinical assessment. The three patient-completed screening questionnaires (PEST, CONTEST and CONTESTjt) were administered, along with other patient-reported measures, and a clinical examination of skin and joints was performed. Participants who demonstrated signs of inflammatory arthritis suggestive of PsA were referred, via their GP, for a further assessment in a secondary care rheumatology clinic. Results: A total of 791 participants attended the screening visit, and 165 participants were judged to have signs and symptoms of inflammatory arthritis, of which 150 were referred for assessment. Of these, 126 were seen and 48 were diagnosed with PsA. The results for each questionnaire were as follows: PEST: sensitivity 0.625 (95% CI 0.482, 0.749), specificity 0.757 (0.724, 0.787); CONTEST: sensitivity 0.604 (0.461, 0.731), specificity 0.768 (0.736, 0.798); and CONTESTjt: sensitivity 0.542 (0.401, 0.676), specificity 0.834 (0.805, 0.859). CONTESTjt demonstrated marginally superior specificity to PEST, though the area under the ROC curve was similar for all three instruments. Conclusion: Minimal differences between the three screening questionnaires were found in this study, and no preferred questionnaire is indicated by these results. The choice of which instrument to choose will depend on other factors, such as simplicity and low patient burden