539 research outputs found
A Self-adaptive Fireworks Algorithm for Classification Problems
his work was supported in part by the National Natural Science Foundation of China under Grants 61403206 and 61771258, in part by the Natural Science Foundation of Jiangsu Province under Grants BK20141005 and BK20160910, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 14KJB520025, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, in part by the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT, under Grant JSGCZX17001, and in part by the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, under Contract SKL2017CP01.Peer reviewedPublisher PD
Swarm Intelligence and Metaphorless Algorithms for Solving Nonlinear Equation Systems
The simplicity, flexibility, and ease of implementation have motivated the
use of population-based metaheuristic optimization algorithms. By focusing
on two classes of such algorithms, particle swarm optimization (PSO)
and the metaphorless Jaya algorithm, this thesis proposes to explore the
capacity of these algorithms and their respective variants to solve difficult
optimization problems, in particular systems of nonlinear equations converted
into nonlinear optimization problems. For a numerical comparison to be
made, the algorithms and their respective variants were implemented and
tested several times in order to achieve a large sample that could be used
to compare these approaches as well as find common methods that increase
the effectiveness and efficiency of the algorithms. One of the approaches
that was explored was dividing the solution search space into several
subspaces, iteratively running an optimization algorithm on each subspace,
and comparing those results to a greatly increased initial population. The
insights from these previous experiments were then used to create a new
hybrid approach to enhance the capabilities of the previous algorithms, which
was then compared to preexisting alternatives.A simplicidade, flexibilidade e facilidade de implementa¸c˜ao motivou o uso
de algoritmos metaheur´ısticos de optimiza¸c˜ao baseados em popula¸c˜oes.
Focando-se em dois destes algoritmos, optimiza¸c˜ao por exame de part´ıculas
(PSO) e no algoritmo Jaya, esta tese prop˜oe explorar a capacidade destes
algoritmos e respectivas variantes para resolver problemas de optimiza¸c˜ao de
dif´ıcil resolu¸c˜ao, em particular sistemas de equa¸c˜oes n˜ao lineares convertidos
em problemas de optimiza¸c˜ao n˜ao linear. Para que fosse poss´ıvel fazer
uma compara¸c˜ao num´erica, os algoritmos e respectivas variantes foram
implementados e testados v´arias vezes, de modo a que fosse obtida uma
amostra suficientemente grande de resultados que pudesse ser usada para
comparar as diferentes abordagens, assim como encontrar m´etodos que
melhorem a efic´acia e a eficiˆencia dos algoritmos. Uma das abordagens
exploradas foi a divis˜ao do espa¸co de procura em v´arios subespa¸cos,
iterativamente correndo um algoritmo de optimiza¸c˜ao em cada subespa¸co,
e comparar esses resultados a um grande aumento da popula¸c˜ao inicial, o
que melhora a qualidade da solu¸c˜ao, por´em com um custo computacional
acrescido. O conhecimento resultante dessas experiˆencias foi utilizado na
cria¸c˜ao de uma nova abordagem hibrida para melhorar as capacidades dos
algoritmos anteriores, a qual foi comparada a alternativas pr´e-existentes
Development of Hybrid PS-FW GMPPT Algorithm for improving PV System Performance Under Partial Shading Conditions
A global maximum power point tracking (MPPT) algorithm hybrid based on Particle Swarm Fireworks (PS-FW) algorithm is proposed which is formed with Particle Swarm Optimization and Fireworks Algorithm. The algorithm tracks the global maximum power point (MPP) when conventional MPPT methods fail due to occurrence of partial shading conditions. With the applied strategies and operators, PS-FW algorithm obtains superior performances verified under simulation and experimental setup with multiple cases of shading patterns
Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units
The unit commitment problem is a complex and essential problem in the power generation field, which is solved to obtain the schedule of a large number of generating units to minimize the operating cost and the fulfillment of consumer load demand. The present work solves the unit commitment problem using quantum-inspired evolutionary algorithms with a novel elitist local search method (QIEA-ELS). The proposed algorithm solves the unit commitment problem efficiently and its applicability is verified on various unit test systems. The constraints are satisfied efficiently to find a feasible solution, the novel elitist search method is used to locally explore the search area around the fittest individual to find a better solution in its vicinity in genotype space represent by qubits. The solution of the unit commitment is carried out considering two small population sizes as suggested in earlier work by other authors using QIEA, though it can be extended using larger population size also. The computational time is also reduced by using the suggested method with a novel elitist local search (ELS) method. The results obtained after applying the proposed algorithm are found to better as compared to other well-known solution techniques
An Adaptive Task Scheduling in Fog Computing
Internet applications generate massive amount of data. For processing the data, it is transmitted to cloud. Time-sensitive applications require faster access. However, the limitation with the cloud is the connectivity with the end devices. Fog was developed by Cisco to overcome this limitation. Fog has better connectivity with the end devices, with some limitations. Fog works as intermediate layer between the end devices and the cloud. When providing the quality of service to end users, scheduling plays an important role. Scheduling a task based on the end users requirement is a tedious thing. In this paper, we proposed a cloud-fog task scheduling model, which provides quality of service to end devices with proper security
CHEMOTAXIS DIFFERENTIAL EVOLUTION OPTIMIZATION TECHNIQUES FOR GLOBAL OPTIMIZATION
Nature inspired and bio-inspired algorithms have been recently used for solving low
and high dimensional search and optimization problems. In this context, Bacterial
Foraging Optimization Algorithm (BFOA) and Differential Evolution (DE) have been
widely employed as global optimization techniques inspired from social foraging behavior
of Escheria coli bacteria and evolutionary ideas such as mutation, crossover, and selection,
respectively.
BFOA employs chemotaxis (tumble and run steps of a bacterium in its lifetime)
activity for local search whereas the global search is performed by elimination-dispersal
operator. Elimination-dispersal operator kills or disperses some bacteria and replaces
others randomly in the search space. This operator mimics bacterium’s death or dispersal
in case of high temperature or sudden water flow in the environment. DE employs the mutation and crossover operators to make a local and a global search
that explore the search space. Exploration and exploitation balance of DE is performed
by two different parameters: mutation scaling factor and crossover rate. These two
parameters along with the number of population have an enormous impact on optimization
performance.
In this thesis, two novel hybrid techniques called Chemotaxis Differential Evolution
Optimization Algorithm (CDEOA) for low dimensions and micro CDEOA (μCDEOA)
for high dimensional problems are proposed. In these techniques, we incorporate the
principles of DE into BFOA with two conditions. What makes our techniques different
from its counterparts is that it is based on two optimization strategies: exploration of a
bacterium in case of its failure to explore its vicinity for food source and exploitation of
a bacterium in case of its achievement to exploit more food source. By means of these
evolutionary ideas, we manage to establish an efficient balance between exploration of
new areas in the search space and exploitation of search space gradients. Statistics of
the computer simulations indicate that μCDEOA outperforms, or is comparable to, its
competitors in terms of its convergence rates and quality of final solution for complex high
dimensional problems
A NOVEL METAHEURISTIC ALGORITHM: DYNAMIC VIRTUAL BATS ALGORITHM FOR GLOBAL OPTIMIZATION
A novel nature-inspired algorithm called the Dynamic Virtual Bats Algorithm (DVBA)
is presented in this thesis. DVBA is inspired by a bat’s ability to manipulate frequency
and wavelength of the emitted sound waves when hunting. A role based search has been
developed to improve the diversification and intensification capability of standard Bat
Algorithm (BA). Although DVBA is inspired from bats, like BA, it is conceptually very
different from BA. BA needs a huge number of population size; however, DVBA employs
just two bats to handle the ”exploration and exploitation” conflict which is known as a
real challenge for all optimization algorithms.
Firstly, we study bat’s echolocation ability and next, the most known bat-inspired
algorithm and its modified versions are analyzed. The contributions of this thesis start
reading and imitating bat’s hunting strategies with different perspectives. In the DVBA, there are only two bats: explorer and exploiter bat. While the explorer bat explores the
search space, the exploiter bat makes an intensive search of the local with the highest
probability of locating the desired target. Depending on their location, bats exchange the
roles dynamically.
The performance of the DVBA is extensively evaluated on a suite of 30 bound-constrained
optimization problems from Congress of Evolutionary Computation (CEC) 2014 and
compared with 4 classical optimization algorithm, 4 state-of-the-art modified bat
algorithms, and 5 algorithms from a special session at CEC2014. In addition, DVBA
is tested on supply chain cost problem to see its performance on a complicated real world
problem. The experimental results demonstrated that the proposed DVBA outperform, or
is comparable to, its competitors in terms of the quality of final solution and its convergence
rates.Epoka Universit
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