This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Chicken Swarm as a Multi Step Algorithm for Global Optimizationinventionjournals
A new modified of Chicken Swarm Optimization (CSO) algorithm called multi step CSO is proposed for global optimization. This modification is reducing the CSO algorithm’s steps by eliminates the parameter roosters, hens and chicks. Multi step CSO more efficient than CSO algorithm to solve optimization problems. Experiments on seven benchmark problems and a speed reducer design were conducted to compare the performance of Multi Step CSO with CSO algorithms and the other algorithms based population such as Cuckoo Search (CS),Particle Swarm Optimization (PSO), Differential Evolution (DE) and Genetic Algorithm (GA). Simulation results show that Multi step CSO algorithm performs better than those algorithms. Multi step CSO algorithm has the advantages of simple, high robustness, fast convergence, fewer control.
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Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Chicken Swarm as a Multi Step Algorithm for Global Optimizationinventionjournals
A new modified of Chicken Swarm Optimization (CSO) algorithm called multi step CSO is proposed for global optimization. This modification is reducing the CSO algorithm’s steps by eliminates the parameter roosters, hens and chicks. Multi step CSO more efficient than CSO algorithm to solve optimization problems. Experiments on seven benchmark problems and a speed reducer design were conducted to compare the performance of Multi Step CSO with CSO algorithms and the other algorithms based population such as Cuckoo Search (CS),Particle Swarm Optimization (PSO), Differential Evolution (DE) and Genetic Algorithm (GA). Simulation results show that Multi step CSO algorithm performs better than those algorithms. Multi step CSO algorithm has the advantages of simple, high robustness, fast convergence, fewer control.
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The built-in R dataset, ChickWeight, with information on 20 chicks on 4 diets was explored. Techniques of probability models and statistical inference was used to compare the effects of diets on the chicks.
Koch’s postulate in reproduction of broiler coccidiosis by co-infection with ...Danielle Ayyash
The purpose of this research is to establish a model of Koch’s postulate for reproducing coccidiosis in broilers by co-infection with eight most common Eimeria spp. involved in this economic disease, in an attempt to use this model in future evaluation of new controlling biologics.
Visceral organ of colored broiler chicken (Gallus domesticus) fed with commer...Innspub Net
This study was conducted atcmU Poultry Production Project, Musuan, and Maramag Bukidnon to evaluate the effect of Black Soldier Fly Larvae (Hermetia illucens) under a free-range condition in the commercial ration on the visceral organ of colored broiler chicken. A total of 12 birds were obtained from a flock of 48 colored broiler chickens from a growth performance study. The treatments were as follows: Treatment 1 = 100g commercial feeds (control), Treatment 2 = 95% commercial feeds + 5% BSF larvae, Treatment 3 = 90% commercial feeds + 10% BSF larvae and Treatment 4 = 85% commercial feeds + 15% BSF larvae. Based on the result of analysis of variance (ANOVA), it showed no significant differences among the parameters of the studied weights of the crop with and without fill, weight of proventriculus without fill, weight of small intestine with and without fill, large intestine with and without fill, caeca with and without fill, weight of heart, and weight of gall bladder. However, the weight of proventriculus plus gizzard with fill, weight of liver, weight of spleen, weight of pancreas revealed significant differences based on Duncan’s Multiple Range Test (DMRT). Furthermore, Black Soldier Fly Larvae (Hermetia illucens) were highly recommended to animals because there were no detrimental observed in the study. This result indicates that using Black Soldier Fly Larvae (Hermetia illucens) as supplementation ration can improve the visceral organ performance of broiler chicken.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
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Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...
Chicken swarm optimization (CSO)
1. A New Bio-inspired Algorithm Chicken Swarm Optimization
Xianbing Meng, Yu Liu, Xiaozhi Gao, and Hengzhen Zhang
Supervisor: Dr. Ahmed ElSawy
Presented by : Abdelrahman Alaa & Mohamed Wagih
2. Abstract
A new bio-inspired algorithm, Chicken Swarm Optimization (CSO), is proposed
for optimization applications. Mimicking the hierarchal order in the chicken
swarm and the behaviors of the chicken swarm, including roosters, hens and
chicks, CSO can efficiently extract the chickens’ swarm intelligence to optimize
problems. Experiments on twelve benchmark problems and a speed reducer
design were conducted to compare the performance of CSO with that of other
algorithms. The results show that CSO can achieve good optimization results in
terms of both optimization accuracy and robustness.
2
3. Agenda
Introduction
General Biology
Chicken Swarm optimization (CSO)
Movement of Chickens
Parametric Analysis
Validation and Comparison
Benchmark Problems Optimization
Speed Reducer Design
Discussion
References
3
5. 5
Introduction
Background
Chickens are kept as food source and Live together in flocks
Communicate using over 30 distinct sounds “clucks, cackles, chirps and cries”
including a lot of information “nesting, food discovery, mating and danger”
Learn through trial, error and previous experience
6. 6
Introduction
CSO mimics the hierarchal order in the chicken swarm
and the behaviors of the chicken swarm.
Hierarchal order? Behaviors of the chicken swarm?
8. 8
Introduction
Chicken swarm can be divided into several groups
each Group contains : 1 Rooster + many hens + many chicks
Competition between different chickens under specific order
9. 9
General Biology
Hierarchal order
A hierarchal order plays a significant role in the social lives of chickens
The preponderant chickens will dominate the weak
More dominant hens that remain near to the head roosters
The More submissive chicken stand at the periphery of the group
Removing or adding chickens from an existing group would causes
a temporary disruption to the social order until a specific hierarchal order
is established
10. 10
General Biology
Hierarchal order
The dominant individuals have priority for food access
Roosters may call their group-mates to eat first when they find food
Gracious behavior also exists in the hens when they raise their children.
However, this is not the case existing for individuals from different groups.
Roosters would emit a loud call when other chickens from a different group
invade their territory
11. 11
General Biology
Hierarchal order In General
The chicken’s behaviors vary with Gender
The head rooster would positively search for food, and fight with chickens
who invade the territory the group inhabits
The dominant chickens would be nearly consistent with the head roosters
to forage for food
The submissive ones, however, would reluctantly stand at the periphery of
the group to search for food. There exist competitions between different
chickens. As for the chicks, they search for the food around their mother
12. 12
Chicken Swarm Optimization (CSO)
Chickens’ behaviors rules
1- Chicken swarm divided into groups
each Group=a dominant , a couple of and
2- Group division methodology. All depends on the fitness values
Best fitness -> worst fitness and the rest are
Each would be the head rooster in a group
The hens randomly choose which group to live in.
The mother-child relationship is also randomly established.
13. 13
Chicken Swarm Optimization (CSO)
3- The hierarchal order, dominance relationship and mother-child relationship
in a group will remain unchanged. Only update every several (G) time steps
4- chicken follow their group-mate rooster to search for food While they
prevent the ones from eating their own food
Assume chickens would randomly steal the good food already found
by others.
The chicks search for food around their mother (hen)
The dominant individuals have advantage in competition for food.
RN “Roosters”, HN “hens”, CN “chicks” and MN “mother hens”
The best RN chickens would be assumed to be roosters
while the worst CN ones would be regarded as chicks
The rest are treated as hens
14. 14
Chicken Swarm Optimization (CSO)
All N virtual chickens, depicted by their positions
at time step t, search for food in a D-dimensional space.
In this work, the optimization problems are the minimal ones
Thus the best RN chickens -> the ones with RN minimal fitness values.
15. 15
Chicken Swarm Optimization (CSO)
Chickens Movement (Roosters)
The roosters with better fitness values have priority for food access than the ones
with worse fitness values.
For simplicity, Roosters with better fitness values can search for food in
a wider range of places than that of the roosters with worse fitness values
Randn (0, 𝝈 𝟐
) is a Gaussian distribution with mean 0 and standard deviation 𝝈 𝟐
𝜀, which is used to avoid zero-division-error, is the smallest constant in the computer
k, a rooster’s index, is randomly selected from the roosters group
f is the fitness value of the corresponding x.
16. 16
Chicken Swarm Optimization (CSO)
Chickens Movement (Hens)
Hens can follow their group-mate roosters to search for food.
They would also randomly steal the good food found by other chickens
Though they would be repressed by the other chickens.
The more dominant hens would have advantage in competing for food than the
more submissive ones
Rand is a uniform random number over [0, 1]
𝒓𝟏 ∈ [𝟏, … . . , 𝑵] index of the rooster, which is the ith hen’s group-mate
𝒓𝟐 ∈ [𝟏, … . . , 𝑵] index of the chicken (rooster or hen ), which is randomly chosen 𝒓𝟏 ≠ 𝒓𝟐
S1= exp(
𝑓 𝑖−𝑓𝑟1
|𝑓 𝑖|+𝜀
) (4) S2= exp(𝑓𝑟2 − 𝑓𝑖) (5)
17. 17
Chicken Swarm Optimization (CSO)
Chickens Movement (Hens)
Obviously 𝑓𝑖 > 𝑓𝑟1 , 𝑓𝑖 > 𝑓𝑟2 , thus S2 <1< S1
Assume S1=0,
then the ith hen would forage for food just followed by other chickens.
The bigger the difference of the two chickens’ fitness values
the smaller S2 and the bigger the gap between the two chickens’ positions is.
Thus the hens would not easily steal the food found by other chickens.
S1 and S2 formulas differs because there exist competitions in a group.
the fitness values of the chickens relative to the fitness value of the rooster
are simulated as the competitions between chickens in a group.
S2= exp(𝑓𝑟2 − 𝑓𝑖) (5)
18. 18
Chicken Swarm Optimization (CSO)
Chickens Movement (Hens)
Suppose S2=0,
then the ith hen would search for food in their own territory.
For the specific group, the rooster’s fitness value is unique.
Thus the smaller the ith hen’s fitness value, the nearer S1 approximates to 1
and the smaller the gap between the positions of the ith hen and its group-mate rooster
Hence the more dominant hens
would be more likely than the more submissive ones to eat
19. 19
Chicken Swarm Optimization (CSO)
Chickens Movement (Chicks)
The chicks move around their mother to forage for food. This is formulated below
𝒙 𝒎,𝒋
𝒕
stands for the position of the ith chick’s mother (𝒎 ∈ [𝟏, 𝑵])
𝑭𝑳(𝑭𝑳 ∈ 𝟎, 𝟐 ) parameter indicates that the chick would follow its mother to forage for food
Consider the individual differences, the FL of each chick would randomly choose between 0 and 2
21. 21
Chicken Swarm Optimization (CSO)
Algorithm
• Individual of chicken swarm population are initialized by using the
following formula
• With lb and ub are lower bound and upper bound of the search
space.
22. 22
Chicken Swarm Optimization (CSO)
Parametric Analysis
There exist six parameters in CSO.
HN would be bigger than RN -> keeping hens is more beneficial for human because only
hens can lay eggs, which can also be the source of food
HN is also bigger than MN -> not all hens would hatch their eggs simultaneously
Though each hen can raise more than one chick, we assume the population of adult chickens
would surpass that of the chicks, CN
As for G, it should be set at an appropriate value, which is problem-based.
If G is very big-> it's not conducive for the algorithm to converge to the global optimal quickly.
If G is very small, the algorithm may trap into local optimal.
After the preliminary test, G ∈ [2,20] may achieve good results for most problem.
In practice, FL ∈ [0.4, 1] usually perform well.
The formula of the chick’s movement can be associated with the corresponding part in DE
If we set RN and MN at 0, thus CSO essentially becomes the basic mutation scheme of DE.
23. 23
Benchmark Problems Optimization
Twelve popular benchmark problems (shown in Table 1) are used to verify the
performance of the CSO compared with that of PSO, DE and BA.
The statistical results have been obtained, based on 100 independent trials, in all
the case studies. The number of iterations is 1,000 in each trial.
For a fair comparison, all of the common parameters of these methods, such as the
population size, dimensions and maximum number of generations, are set to be the
same. The related parameters of these algorithms are showed at Table 2
Validation and Comparison
26. 26
Benchmark Problems Optimization
The superiority of CSO over PSO, BA and DE should be the case.
If we set RN = CN = 0, and let S1, S2 be the parameters like c1 and c2 in PSO, thus
CSO will be similar to the standard PSO. Hence CSO can inherit many advantages
of PSO and DE.
Moreover, the chickens’ swarm intelligence can be efficiently extracted in CSO.
Given the diverse laws of the chickens' motions and cooperation between the
multigroups, the search space can be efficiently explored.
Under the specific hierarchal order, the whole chicken swarm may behave like a
team to forage for food, which can be associated with the objective problems to
be optimized. All of these merits enhance the performance of CSO.
Validation and Comparison
29. 29
Speed Reducer Design – design Gearbox
Which can be rotated at its most efficient speed.
The gearbox is described by
The face width 𝑏(𝑋1)
Module of teeth 𝑚(𝑋2)
Number of teeth in the pinion 𝑍 𝑋3
Length of the first shaft between bearings ℎ1 𝑋4
Length of the second shaft between bearings ℎ2 𝑋5
Diameter of the first shaft 𝑑1 𝑋6
Diameter of the first shaft 𝑑2 𝑋7
Speed Reducer Design optimization is to minimize its total weight, subject
to constraints on bending stress of the gear teeth, surface stress, transverse
deflections of the shafts, and stresses in the shafts
Validation and Comparison
30. 30
Application - Speed Reducer Design – design Gearbox
This problem can be formulated as follows
Validation and Comparison
31. 31
Validation and Comparison
Speed Reducer Design – Design Gearbox
the results achieved by CSO and other algorithms.
CSO’s results outperform all the results achieved by other methods
in terms of both optimization accuracy and robustness
which indicates that the solution is feasible.
32. 32
Discussion
The performance of CSO is compared with that of the PSO, DE and BA on twelve
benchmark problems.
Experiments show that CSO outperforms the PSO, DE and BA in terms of both
optimization accuracy and robustness.
Moreover, CSO can efficiently solve the speed reducer design, which endues the CSO
with a promising prospect of further studying.
One of the reasons that CSO has very promising performance is that CSO inherits major
advantages of many algorithms. PSO and the mutation scheme of DE are the special
cases of the CSO under appropriate simplifications.
What is more significant for the superiority of the CSO is that the chickens’ swarm
intelligence can be efficiently extracted to optimize problems.
The chickens' diverse movements can be conducive for the algorithm to strike a good
balance between the randomness and determinacy for finding the optima.
33. 33
Discussion
The whole chicken swarm consists of several groups, namely multi-swarm. Through
integration of the hierarchal order, chickens of the different groups may behave as a team
and coordinate themselves to forage for food. Thus CSO can behave intelligently to
optimize problems efficiently.
The innovation in this paper not only lies in efficiently extracting the chickens’ swarm
intelligence to optimize problems, but also making CSO innate multi-swarm method.
Multi-swarm technique is usually used to enhance performance of the population-
based algorithm. As an innate multi-swarm algorithm, various multi-swarm techniques can
be used to develop the different variants of CSO. Thus CSO has good extensibility.
Moreover, from the parametric analysis, the population of the hens is the biggest in the
swarm. Thus the performance of CSO largely depends on how the hens’ swarm
intelligence can be extracted to optimize problems.
34. 34
Discussion
The motion of the hens can be adaptively controlled according to the fitness value of
the problem itself.
With the dynamical hierarchal order, the hens swarm can be updated. Hence CSO has
the self adaptive ability to solve the optimization problems.
More comprehensive analyses on the CSO are still need to be investigated in the future.
Moreover, we can consider there exist several roosters in a group and dynamically
adjust the population of the hens and chicks in each group.
It’s also significant to tune the related parameters for enhancing the algorithm
performance, and design the variants of the CSO to solve many optimization applications.
35. 35
References
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