9 research outputs found
Multi-population methods with adaptive mutation for multi-modal optimization problems
open access journalThis paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms
Adaptive Mutation Operators for Evolutionary Algorithms
Evolutionary algorithms (EAs) are a class of stochastic search and optimization algorithms
that are inspired by principles of natural and biological evolution. Although
EAs have been found to be extremely useful in finding solutions to practically intractable
problems, they suffer from issues like premature convergence, getting stuck
to local optima, and poor stability. Recently, researchers have been considering
adaptive EAs to address the aforementioned problems. The core of adaptive EAs is
to automatically adjust genetic operators and relevant parameters in order to speed
up the convergence process as well as maintaining the population diversity.
In this thesis, we investigate adaptive EAs for optimization problems. We study
adaptive mutation operators at both population level and gene level for genetic
algorithms (GAs), which are a major sub-class of EAs, and investigate their performance
based on a number of benchmark optimization problems. An enhancement
to standard mutation in GAs, called directed mutation (DM), is investigated in
this thesis. The idea is to obtain the statistical information about the fitness of
individuals and their distribution within certain regions in the search space. This
information is used to move the individuals within the search space using DM. Experimental
results show that the DM scheme improves the performance of GAs on
various benchmark problems.
Furthermore, a multi-population with adaptive mutation approach is proposed to
enhance the performance of GAs for multi-modal optimization problems. The main
idea is to maintain multi-populations on different peaks to locate multiple optima for
multi-modal optimization problems. For each sub-population, an adaptive mutation
scheme is considered to avoid the premature convergence as well as accelerating the
GA toward promising areas in the search space. Experimental results show that the
proposed multi-population with adaptive mutation approach is effective in helping
GAs to locate multiple optima for multi-modal optimization problems
Adaptive mutation operators for evolutionary algorithms
Evolutionary algorithms (EAs) are a class of stochastic search and optimization algorithms that are inspired by principles of natural and biological evolution. Although EAs have been found to be extremely useful in finding solutions to practically intractable problems, they suffer from issues like premature convergence, getting stuck to local optima, and poor stability. Recently, researchers have been considering adaptive EAs to address the aforementioned problems. The core of adaptive EAs is to automatically adjust genetic operators and relevant parameters in order to speed up the convergence process as well as maintaining the population diversity. In this thesis, we investigate adaptive EAs for optimization problems. We study adaptive mutation operators at both population level and gene level for genetic algorithms (GAs), which are a major sub-class of EAs, and investigate their performance based on a number of benchmark optimization problems. An enhancement to standard mutation in GAs, called directed mutation (DM), is investigated in this thesis. The idea is to obtain the statistical information about the fitness of individuals and their distribution within certain regions in the search space. This information is used to move the individuals within the search space using DM. Experimental results show that the DM scheme improves the performance of GAs on various benchmark problems. Furthermore, a multi-population with adaptive mutation approach is proposed to enhance the performance of GAs for multi-modal optimization problems. The main idea is to maintain multi-populations on different peaks to locate multiple optima for multi-modal optimization problems. For each sub-population, an adaptive mutation scheme is considered to avoid the premature convergence as well as accelerating the GA toward promising areas in the search space. Experimental results show that the proposed multi-population with adaptive mutation approach is effective in helping GAs to locate multiple optima for multi-modal optimization problems.EThOS - Electronic Theses Online ServiceFinancial support received from the University of SindhJamshoroSindhPakistanGBUnited Kingdo
Conceptual framework for measuring the acceptance of Smartwatch to check blood pressure and pulse transmission time
Innovative research and development in Information
Communication and Technology have reshaped our daily
life. Use of ICT tools in medicine has great impact on our
daily life. Nowadays our life is at is at major risk of health
diseases. Hypertension is becoming a chronic disease in our
society. To avoid hypertension, use of Smartwatch is better
option. Because most of the peoples are not aware of
diseases which occurs due to the blood pressure. If these
diseases are not diagnosed at early stage, then it is too
difficult to control. So, by using Smartwatch they can get
update of their health and will get proper treatment. But
technology itself cannot guarantee the usage of technology
so this research is to identify the motivating external factors
that affect the students to use Smartwatch for check blood
pressure and pulse transit time. Hyderabad and Jamshoro
users of smartwatch was the target population of this
research. Stratified random sampling techniques was used
in this research. Technology acceptance model (TAM) was
used with three external factors Self efficacy, Subjective
norms and Trust. Quantitative research methodology has
been used for this research. After data collection SPSS and
SmartPLS was used for data analysis and hypothesis
testing
Analysis of Energy and Network Cost Effectiveness of Scheduling Strategies in Datacentre
In parallel and distributed computing, cloud computing is progressively replacing the traditional computing paradigm. The cloud is made up of a set of virtualized resources in a data center that can be configured according to usersโ needs. In other words, cloud computing faces the problem of a huge number of users requesting unlimited jobs for execution on a limited number of resources, which increases energy consumption and the network cost of the system. This study provides a complete analysis of classic scheduling techniques specifically for handling data-intensive workloads to see the effectiveness of the energy and network costs of the system. The workload is selected from a real-world data center. Moreover, this study offers the pros and cons of several classical heuristics-based job scheduling techniques that take into account the time and cost of transferring data from multiple sources. This study is useful for selecting appropriate scheduling techniques for appropriate environments