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Human learning principles inspired particle swarm optimization algorithms
No full textThese days, the nature of global optimization problems, especially for engineering systems
has become extremely complex. For these types of problems, nature inspired search
based algorithms are providing much better solutions compared with other classical optimization
methods. Among them, the Particle Swarm Optimization (PSO) algorithm has
been mostly preferred due to its simplicity and ability to provide better solutions. PSO
algorithm simulates the social behaviour of a bird swarm in search of food where the
birds are modelled as particles. The limitations associated with PSO have been extensively
studied and different modifications, variations and refinements to PSO have been
proposed in the literature for enhancing its performance. The idea of utilizing intelligent
swarms motivated towards exploring human cognitive learning principles for PSO. As discussed
in learning psychology, human beings are known to be intelligent and have good
social cognizance. Therefore, any optimization technique employing human-like learning
strategies should prove to be more effective. This thesis addresses the use of human
learning principles inspired strategies for the PSO algorithm. The major contributions
of the thesis are:
• Self-Regulating Particle Swarm Optimization (SRPSO) algorithm.
• Dynamic Mentoring and Self-Regulation based Particle Swarm Optimization (DMeSRPSO)
algorithm.
• Directionally Driven Self-Regulating Particle Swarm Optimization (DD-SRPSO)
algorithm.
• Incorporation of a constraint handling mechanism in the structure of the DDSRPSO
algorithm.
The Self-Regulating Particle Swarm Optimization (SRPSO) algorithm is inspired
from the human self-learning principles. SRPSO utilizes self-regulation and self-perception
based learning strategies to achieve an enhanced exploration and a better exploitation.
The self-regulated inertia weights are employed only for the best particle whereas all
the other particles perform search employing self-perception of the global best search direction. The perception is dynamically changed in every iteration for intelligent exploitation.
The effect of human learning strategies on the particles has been studied
using CEC2005 benchmark problems and the performance has been compared with the
state-of-the-art PSO variants. The results clearly indicate that SRPSO converge faster
closer to the global optimum with a 95% confidence level.
Further, human beings utilize multiple information processing strategies during the
learning process and collaborate with each other for better decision making. Integration
of socially shared information processing will further enhance the performance. Therefore,
a new algorithm referred to as Dynamic Mentoring and Self-Regulation based Particle
Swarm Optimization (DMeSR-PSO) algorithm has been proposed incorporating the
concept of mentoring together with the self-regulation. Here, the particles are divided into
three groups consisting of mentors, mentees and independent learners. The elite particles
are grouped as mentors to guide the poorly performing particles of the mentees group.
The independent learners perform search using self-perception based learning strategy of
the SRPSO algorithm. Tested on both the unimodal and multimodal CEC2005 benchmark
problems the DMeSR-PSO has shown improved convergence than the SRPSO algorithm.
Further, the robustness of the algorithm has been tested on CEC2013 problems
and eight real-world optimization problems from CEC2011. The results indicate that
DMeSR-PSO is significantly better than other PSO variants and other population based
optimization algorithms with a 95% confidence level, yielding an effective optimization
algorithm for real-world applications.
Both SRPSO and DMeSR-PSO are rotationally variant algorithms and therefore the
performances have not been significant on the rotated problems. To overcome this, a directionally
updated and rotationally invariant SRPSO algorithm has also been developed
named as Directionally Driven SRPSO (DD-SRPSO) algorithm. Here, the poorly performing
particles are equipped with complete social perception guidance. Other particles
are randomly selected to perform search either by using self-perception based learning
strategy of SRPSO or by applying a rotation invariant strategy. The performance
of DD-SRPSO tested on rotated problems from CEC2013 proves that DD-SRPSO is
significantly better than SRPSO. Its performance, compared with other algorithms on
CEC2013 benchmark problems clearly indicates that DD-SRPSO is significantly better
than selected algorithms on a wide range of problems.
Further, a new constraint handling mechanism has been incorporated in the DDSRPSO
structure referred to as DD-SRPSO with constraint handing mechanism (DDSRPSOCHM).
Next, the application of DD-SRPSO-CHM in optimizing multi-stage launch vehicle
configuration has been studied. In a multi-stage launch vehicle configuration, the
multiple objectives are converted into a single objective with constraints and these are
efficiently handled by DD-SRPSO-CHM. Comparative analysis on the problem suggests
that DD-SRPSO is converging faster towards the solution.
By incorporating human-like behaviour in the PSO algorithm, the developed variants
have shown a faster convergence closer to the optima over a diverse set of problems indicating that the algorithms are potential choice for complex real-world applications.
In the future, the algorithm will be extended for solving multi-objective optimization
problems. The equality constraint handling mechanism has already been implemented in
the DD-SRPSO algorithm which can be further extended for the inequality constraints.
Furthermore, more human learning strategies can be explored for performance enhancement.Doctor of Philosophy (SCE
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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