10 research outputs found
Economic load dispatch solutions considering multiple fuels for thermal units and generation cost of wind turbines
In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The suggested IPSO is the modified version of Particle swarm optimization (PSO) by changing velocity and position updates. The five best solutions are employed to replace the so-far best position of each particle in velocity update mechanism and the five best solutions are used to replace previous position of each particle in position update. In addition, constriction factor is also used in the suggested IPSO. PSO, constriction factor-based PSO (CFPSO) and bat optimization algorithm (BOA) are also run for comparisons. Two systems are used to run the four methods. The first system is comprised of nine TUs with multiple fuels and one wind farm. The second system is comprised of eight TUs with multiple fuels and two WFs. From the comparisons of results, IPSO is much more powerful than three others and it can find optimal power generation with the lowest total power generation cost
REVISIÓN SOBRE ALGORITMOS DE OPTIMIZACIÓN MULTI-OBJETIVO GENÉTICOS Y BASADOS EN ENJAMBRES DE PARTÍCULAS
El enfoque evolutivo como también el comportamiento social han mostrado ser una muy buena alternativa en los problemas de optimización donde se presentan varios objetivos a optimizar. De la misma forma, existen todavía diferentes vias para el desarrollo de este tipo de algoritmos. Con el fin de tener un buen panorama sobre las posibles mejoras que se pueden lograr en los algoritmos de optimización bio-inspirados multi-objetivo es necesario establecer un buen referente de los diferentes enfoques y desarrollos que se han realizado hasta el momento.En este documento se revisan los algoritmos de optimización multi-objetivo más recientes tanto genéticos como basados en enjambres de partículas. Se realiza una revisión critica con el fin de establecer las características más relevantes de cada enfoque y de esta forma identificar las diferentes alternativas que se tienen para el desarrollo de un algoritmo de optimización multi-objetivo bio-inspirado.Review about genetic multi-objective optimization algorithms and based in particle swarmABSTRACTThe evolutionary approach as social behavior have proven to be a very good alternative in optimization problems where several targets have to be optimized. Likewise, there are still different ways to develop such algorithms. In order to have a good view on possible improvements that can be achieved in the optimization algorithms bio-inspired multi-objective it is necessary to establish a good reference of different approaches and developments that have taken place so far. In this paper the algorithms of multi-objective optimization newest based on both genetic and swarms of particles are reviewed. Critical review in order to establish the most relevant characteristics of each approach and thus identify the different alternatives have to develop an optimization algorithm multi-purpose bio-inspired design is performed.Keywords: evolutionary computation, evolutionary multi-objective optimization
Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated
initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in
the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art
multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing
Bioinformatics Applications Based On Machine Learning
The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems
MULTI-OBJECTIVE DIFFERENTIAL EVOLUTION: MODIFICATIONS AND APPLICATIONS TO CHEMICAL PROCESSES
Ph.DDOCTOR OF PHILOSOPH
Roteamento multicast multisessão: modelos e algoritmos
Multicast Technology has been studied over the last two decades and It has shown to be a
good approach to save network resources. Many approaches have been considered to solve
the multicast routing problem considering only one session and one source to attending
session‘s demand, as well, multiple sessions with more than one source per session. In
this thesis, the multicast routing problem is explored taking in consideration the models
and the algorithms designed to solve it when where multiple sessions and sources. Two
new models are proposed with different focuses. First, a mono-objective model optimizing
residual capacity, Z, of the network subject to a budget is designed and the objective is to
maximize Z. Second, a multi-objective model is designed with three objective functions:
cost, Z and hops counting. Both models consider multisession scenario with one source
per session. Besides, a third model is examined. This model was designed to optimize
Z in a scenario with multiple sessions with support to more than one source per session.
An experimental analysis was realized over the models considered. For each model, a set
of algorithms were designed. First, an ACO, a Genetic algorithm, a GRASP and an ILS
algorithm were designed to solve the mono-objective model – optimizing Z subject to a
budget. Second, a set of algorithm were designed to solve the multi-objective model. The
classical approaches were used: NSGA2, ssNSGA2, SMS-EMOA, GDE3 and MOEA/D.
In addition, a transgenetic algorithm was designed to solve the problem and it was compared
against the classical approaches. This algorithm considers the use of subpopulations
during the evolution. Each subpopulation is based on a solution construction operator
guided by one of the objective functions. Some solutions are considered as elite solutions
and they are considered to be improved by a transposon operator. Eight versions of the
transgenetic algorithm were evaluated. Third, an algorithm was designed to solve the
problem with multiple sessions and multiple sources per sessions. This algorithm is based
on Voronoi Diagrams and it is called MMVD. The algorithm designed were evaluated on
large experimental analysis. The sample generated by each algorithm on the instances
were evaluated based on non-parametric statistical tests. The analysis performed indicates
that ILS and Genetic algorithm have outperformed the ACO and GRASP. The comparison between ILS and Genetic has shown that ILS has better processing time performance.
In the multi-objective scenario, the version of Transgenetic called cross0 has
shown to be statistically better than the other algorithms in most of the instances based
on the hypervolume and addictive/multiplicative epsilon quality indicators. Finally, the
MMVD algorithm has shown to be better than the algorithm from literature based on the
experimental analysis performed for the model with multiple session and multiple sources
per session.A tecnologia multicast tem sido amplamente estudada ao longo dos anos e apresenta-se
como uma solução para melhor utilização dos recursos da rede. Várias abordagens já
foram avaliadas para o problema de roteamento desde o uso de uma sessão com apenas
uma fonte a um cenário com múltiplas sessões e múltiplas fontes por sessão. Neste trabalho,
é feito um estudo dos modelos matemáticos para o problema com múltiplas sessões
e múltiplas fontes. Dois modelos matemáticos foram propostos: uma versão multissessão
mono-objetivo que visa a otimização da capacidade residual sujeito a um limite de
custo e uma versão multiobjetivo com três funções-objetivo. Ambos os modelos levam em
conta o cenário multissessão com uma fonte por sessão. Além disso, um estudo algorítmico
foi realizado sobre um modelo da literatura que utiliza múltiplas fontes por sessão.
Três conjuntos de algoritmos foram propostos. O primeiro conjunto trata do problema
mono-objetivo proposto e considera as abordagens ACO, Genético, GRASP e ILS. O segundo
conjunto consiste dos algoritmos propostos para o modelo multiobjetivo. Foram
projetados os seguintes algoritmos: NSGA2, ssNSGA2, GDE3, MOEA/D e SMS-EMOA.
Além disso, foi projetado um algoritmo transgenético com subpopulações baseadas em
operadores de criação de solução direcionados por objetivos do problema. Também foi
utilizado o conceito de soluções de elite. No total, 8 versões do algoritmo transgenético foram
avaliadas. O terceiro conjunto de algoritmos consiste da heurística MMVD proposta
para o modelo da literatura com múltiplas fontes por sessão. Esta heurística é baseada no
uso de diagramas de Voronoi. O processo experimental foi realizado com amplo número
de instâncias configuradas de modo a avaliar diferentes situações. Os resultados foram
comparados utilizando métodos estatísticos não-paramétricos. A análise final indicou que
o ILS e o Genético obtiveram resultados muito similares, entretanto o ILS possui melhor
tempo de processamento. A versão cross0 do algoritmo transgenético obteve o melhor
resultado em praticamente todos os cenários avaliados. A heurística MMVD obteve excelentes
resultados sobre algoritmos da literatura
Developing a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated multiprecision weights of hybrid classifiers
A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyWith the advent of machine learning (ML) techniques, various algorithms have been applied in
previous studies to develop models for predicting soil fertility status. However, these models are
observed to use varying fertility target classes, and variations have been reported in these models'
predictive performances. As a result, practical applications of these models for obtaining the most
accurate predictions may become hindered. While the weighted voting ensemble (WVE) ML
technique can be used to improve soil fertility status prediction by aggregating individual models
prediction, guaranteeing finding of an optimal WVE assignment weights is challenging. Whereas
a brute exhaustive search procedure can be applied for the mentioned task, there is a lack of
exploration on the exploitation of automated classifiers' precise weights combinations as search
spaces for successful optimization. This research aims to develop a high-performance soil
fertility status prediction voting ensemble using brute exhaustive optimization in automated
1EXP(-)Z+ multi-precision weights of hybrid classifiers. Soil chemical properties and ML
modeling algorithms for modeling soil fertility status were identified. Base hybrid ML
classification models for predicting soil fertility status were evaluated using Tanzania as a case
study. Finally, the base ML hybrids WVE models were optimized using brute exhaustive search
procedure’s novel developed search spaces generation algorithm for guaranteed optimal solution
finding. The research was designed using design science research methodology, with the
application of unsupervised machine learning K-mean algorithm with a knee detection method
to find the optimal number of soil fertility status target classes, and supervised learning
algorithms were applied to model classifiers for those optimal classes. Three soil fertility target
classes were identified by clustering technique. The model achieved on test data a predictive
accuracy of 98.93%, with respective AUC of 82%, 83%, and 87% for low, medium, and high
soil fertility targets classes. Whereas these performances are observed higher compared to models
in previous studies, 92% correct classifications were obtained on validation against external
unseen laboratory-based tested soil results. Therefore, soil testing laboratories and farmers should
consider using the model to smartly manage soil fertility which may lead to improved crop
growth and productivity. The government could set agricultural-related policies that require the
use of the model by farmers with the provision of agricultural inputs subsidies. Future work could
be to develop an integrated real-time web and mobile application for providing farmers with soil
fertility status information
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
A complex systems approach to education in Switzerland
The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance