Shuffled Complex Evolution combined with Stochastic Ranking for Reservoir Scheduling PhD Dissertation Writing Services - Phdassistance.com

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Shuffled Complex Evolution Combined With
Stochastic Ranking for Reservoir Scheduling
An Academic presentation by Dr. Nancy Agens,
Head, Technical Operations, Phdassistance
Group www.phdassistance.com Email
info_at_phdassistance.com
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TODAY'S DISCUSSION
In Brief Introduction Shuffled complex evolution
(SCE) SCE-SR Shuffled Complex Evolution -
Stochastic Ranking Algorithm SR Stochastic
Ranking SCE-SR Algorithm Two Main
Characteristics of SCE-SR Four Vital Parameters
of SCE Criteria for SCE-SR Conclusion Future
Scope
Outline
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In Brief

• You will find the best dissertation research
  areas / topics for future researchers enrolled in
  Engineering and technology.
• In order to identify the future research topics,
  we have reviewed the Engineering literature
  (recent peer-reviewed studies) on Shuffled
  Complex Evolution and Stochastic Ranking for
  Reservoir Scheduling
• Nature-Inspired Optimization Algorithm is the
  recent trend in Cloud technology.
• Shuffled Complex Evolution Algorithm is one of
  Nature-Inspired Optimization Algorithm.
• Shuffled Complex Evolution Algorithm is used for
  Reservoir Scheduling and Stochastic Ranking.

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Introduction
Water is one of the most valuable resources and
humans have built dams to optimize the use of
this precious resource. Dams are a
life-sustaining resource for people throughout
the world. These dams have internally water
storage spaces called reservoirs, and the
operation priorities of these reservoirs are
based on a sequence of rules to recognize the
amount of water stored and released according to
system constraints. The process of prioritizing
the reservoir operation is known as "reservoir
scheduling" and we use various machine learning
methods or algorithms for reservoir scheduling.
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Shuffled complex evolution (SCE) is a method,
where
SHUFFLED COMPLEX EVOLUTION (SCE)
its general purpose is global optimization. The
SCE algorithm is capable of finding optimum
globally and it does not rely on the availability
of an explicit expression for the objective
function or the derivatives. Another method
stochastic ranking (SR) is capable of
balancing objectives and penalty functions and is
highly competitive compared to other
methods. The application of a "Shuffled Complex
Evolution- Stochastic Ranking (SCE-SR)"
therefore provides an effective solution to the
above mentioned problems.
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SCE combines the strengths of the simplex method
and the complex algorithm of competitive
evolution and SR is free from complicated
parameter tuning, The SCE-SR takes advantage of
both.
SCE-SR
SCE-SR makes SCE suitable for constrained
reservoir scheduling problems and may achieve
global convergence properties.
The SCE-SR method is an efficient and effective
method to optimize hydropower generation and
quickly identify feasible areas, with adequate
global convergence properties and robustness.
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Shuffled Complex Evolution - Stochastic
Ranking Algorithm
SCE algorithm uses the concept of complex
shuffling to solve the non- linear optimization
problem.
The method involves the following terminologies
Points (candidate solutions),
Population (the community containing all points),
Complex (the community containing several points
Partitioned from the sample),
Complex shuffling (points in complexes reassigned
and mixed to generate a new community).
SCE Algorithm
One of the main components of SCE is the CCE
algorithm, which can be described briefly as
follows Contd..
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Construction of a sub-complex (containing q
points) according to the trapezoidal probability
distribution.
Ranking identification of the worst point u of
the sub-complex and computation of the centroid
g of the q-1 points without including the worst
one. Reflection reflection of point u through
the centroid to generate a new point r and
calculation of its objective function value fr.
If the newly generated point r is within the
feasible space and frgtfu, where fu is the
objective function value of point u, u is
replaced with r, and the process moves to step
(6). Otherwise, it goes to step (4). Contd..
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Contraction determination of a point c halfway
between the centroid and the worst point, and
then calculation of fc. If point c is within the
feasible space and fcgtfu, u is replaced with the
contraction point c and the process goes to step
(6). Otherwise, it goes to step
(5). Mutation random generation of a point z
within the feasible space and replacement of the
worst point with z. Steps (2) through (5) are
repeated a time, where a1 is the number of
consecutive offspring generated by each
sub-complex. Steps (1) through (6) are repeated
ß times, where ß1 is the number of evolution
steps taken by each complex before complexes are
shuffled.
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Figure 1 Flow Chart of SCE-SR Algorithm
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The SR is capable of balancing objective and
penalty functions and improving the search
performance.
SR Stochastic Ranking

• The main idea is to compare two adjacent
  individuals according to the objective function
  values or the degree of constraint violations by
  introducing a predetermined parameter Pf.

An increase in the number of ranking sweeps (N)
is effectively equivalent to changing parameter .
Contd..
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Thus, the number of ranking sweeps is fixed to N
s (number of points in sample population
generated by SCE), and is adjusted within 0, 1
to achieve the best performance. The comparison
mechanism of two adjacent individuals can be
briefly described as follows if both
individuals are feasible, or a randomly generated
number w?0,1 is less than Pf, they are
compared according to the objective function
values otherwise, they are compared based on the
degree of constraint violations. Ranking of the
whole sample population is then achieved through
a bubble-like procedure.
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Thus, the number of ranking sweeps is fixed to N
s (number of points in sample population
generated by SCE), and is adjusted within 0, 1
to achieve the best performance.
SCE-SR Algorithm
The comparison mechanism of two adjacent
individuals can be briefly described as follows
if both individuals are feasible, or a randomly
generated number w?0,1 is less than Pf, they
are compared according to the objective function
values otherwise, they are compared based on
the degree of constraint violations.
Ranking of the whole sample population is then
achieved through a bubble-like procedure.
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The combination of the deterministic approach and
competitive evolution.
Two Main Characteristics of SCE-SR
This is conducive to directing the search in an
improving direction and improving global
convergence efficiency by making use of
information carried by both feasible and
non-feasible individuals. The combination of the
probabilistic approach and complex shuffling.
This guarantees the survivability of individuals
and the flexibility and robustness of the
algorithm.
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Four Vital Parameters of SCE
The number of points in a complex, m2n1, where
n is the dimension of the decision vector,
The number of points in a sub-complex, qn1,
The number of consecutive offspring generated by
each sub- complex, a1, The number of
iterations taken by each complex, ßm.
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For SR, the required range of the parameter is
0.4ltPflt0.5, and for this method it is set to
0.45. The SCE-SR method is terminated whenever
one of the following convergence criteria is
satisfied
Criteria for SCE-SR
(1) The objective function value is not significan
tly improved after j times of iterations. Its
expression is as follows
Contd..
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(2) The interval of variables is small enough.
Its expression is as follows
(3) The cumulative number of objective function
calls (NOFC) reaches the predetermined value.
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Shuffled Complex Evolution - Stochastic Ranking
Algorithm (SCE-SR) optimization method can solve
the complex constrained reservoir scheduling
problems.
When the result of SCE-SR is compared with the
traditional methods against hydropower
scheduling problem in both single and multi-
reservoir system with different inflow scenarios
and population sizes.
Conclusion
SCE-SR can converge the global optima in a
consistent, efficient and fast way with both
objectives and constraints considered. This
SCE-SR method will pave the way for the
application of unconstrained algorithm in this
field as this is an effective, efficient and
reliable optimization method for solving
reservoir scheduling problems.
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It is used in photovoltaic (PV) Model for
parameter extraction problem (Chen et al., 2019).
Future Scope
The research on carbon fiber-epoxide composite
pyrolysis from hydrogen tank(Liu et al., 2019).
It is used in Solar cell models parameter
extraction (Gao et al., 2018).
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