Learn Sperm Swarm Optimizer Step-By-Step Using Examples.
Video Chapters: Sperm Swarm Optimizer
00:00 Introduction
01:20 Topic Covered
01:41 Fertilization Process
07:02 Sperm Swarm Optimizer Simulation
10:14 Sperm Swarm Optimizer Steps
15:27 Conclusion
The Sperm Swarm Optimization (SSO) algorithm is a method inspired by how sperm move toward an egg during fertilization. Swarm Movement: Each sperm (candidate solution) is represented as a point in a multidimensional search space. The sperms work together to find the best solution (the egg). Each sperm's position (location) is adjusted based on its current velocity, its personal best, and the global best.The algorithm keeps updating positions and velocities until an optimal solution is found or a stopping condition is met.
Exploration and Exploitation:
Exploration: Randomized factors (pH and temperature) allow wide search.
Exploitation: Personal and global bests refine the search toward the optimal solution.
The Sperm Swarm Optimization algorithm is a simulation of the natural fertilization process of sperm competing to fertilize an egg. Fertilization in humans is the process where a sperm cell from a male unites with an egg (ovum) from a female to form a fertilized egg or zygote, which eventually develops into a fetus. During sexual intercourse, millions of sperm are released into the woman's vagina.From the vagina, the sperm swim through the cervix and into the uterus. Now, they are in a race to reach the fallopian tube, where the egg might be.The egg releases specific chemical signals into the surrounding area (this is called "chemotaxis"), which attract the sperm toward it. Itās like the egg is sending out a kind of "invitation," saying, "Hey, come over here!" These chemicals guide the sperm in the right direction. As millions of sperm swim toward the egg, they are guided by these chemical signals, and eventually, the fastest and most persistent sperm reaches the egg and attempts to penetrate it.
pH and temperature both play important roles in the survival and functionality of sperm. These factors affect sperm's ability to swim, survive, and ultimately reach the egg.
Vaginal pH: The vagina is naturally acidic (with a pH around 4-5). This acidic environment can be harmful to sperm, and many sperm will not survive long in the vaginal canal. However, during ovulation, the cervical mucus becomes less acidic, creating a more alkaline environment, which is much more friendly for sperm. This helps sperm survive longer and move more easily toward the egg.
Cervical Mucus: During the fertile window (when ovulation occurs), the mucus around the cervix becomes thinner, clearer, and more alkaline. This creates a better path for the sperm, allowing them to swim more easily through the cervix and into the uterus.
Seminal Fluid pH: Seminal fluid (the liquid that carries sperm) has a slightly alkaline pH (around 7.2 to 8.0), which helps protect the sperm from the acidic environment of the vagina.
Optimal Temperature for Sperm: Sperm are produced in the testicles at a temperature that is slightly lower than body temperatureāaround 34-35Ā°C (93-95Ā°F). The testicles are located outside the body for this reason, as the slightly cooler temperature helps sperm production. If the testicles become too warm (for example, due to tight clothing, hot tubs, or high fever), sperm production can be negatively impacted, and the sperm's motility can decrease.
PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. - PART 2 [Example 2] 1.) Initialize Population [Current Iteration (t) = 0] Population Size = 4; š„š : (i = 1,2,3,4) and (t = 0) š„1 =1.3; š„2=4.3; š„3=0.4; š„4=ā1.2 2.) Fitness Function u...
Cuckoo Search Algorithm - Metaheuristic Optimization Algorithm What is Cuckoo Search Algorithm? Cuckoo Search Algorithm is a Meta-Heuristic Algorithm. Cuckoo Search Algorithm is inspired by some Cuckoo species laying their eggs in the nest of other species of birds. In this algorithm, we have 2 bird Species. 1.) Cuckoo birds 2.) Host Birds (Other Species) What if Host Bird discovered cuckoo eggs? Cuckoo eggs can be found by Host Bird. Host bird discovers cuckoos egg with Probability of discovery of alien eggs. If Host Bird Discovered Cuckoo Bird Eggs. The host bird can throw the egg away. Abandon the nest and build a completely new nest. Mathematically, Each egg represent a solution and it is stored in the host bird nest. In this algorithm Artificial Cuckoo Birds are used. Artificial Cuckoo can lay one egg at a time. We will replace New and better solutions with less fit solutions. It means eggs that are more similar to host bird has opportunity to de...
Particle Swarm Optimization (PSO) is a p opulation-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm. In Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. After a number of iterations agents / particles will find out optimal solution in the search space. Q. What is PSO? A. PSO is a computational method that Optimizes a problem. Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to ...
Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
Local Binary Pattern Introduction to Local Binary Pattern (LBP) Q. What is Digital Image? A. Digital images are collections of pixels or numbers ( range from 0 to 255). Q. What is Pixel? A. Pixel is the smallest element of any digital image. Pixel can be categorized as Dark Pixel and Bright Pixel. Dark pixels contain low pixel values and bright pixels contain high pixel values. Q. Explain Local Binary Pattern (LBP)? A. Local binary pattern is a popular technique used for image processing. We can use the local binary pattern for face detection and face recognition. Q. What is LBP Operator? A. LBP operator is an image operator. We can transform images into arrays using the LBP operator. Q. How LBP values are computed? A. LBP works in 3x3 (it contain a 9-pixel value ). Local binary pattern looks at nine pixels at a time. Using each 3x3 window in the digital image, we can extract an LBP code. Q. How to Obtain LBP operator value? A. LBP operator values can be obtained by ...
Grey Wolf Optimization Algorithm (GWO) Grey Wolf Optimization Grey Wolf Optimization Algorithm is a metaheuristic proposed by Mirjaliali Mohammad and Lewis, 2014. Grey Wolf Optimizer is inspired by the social hierarchy and the hunting technique of Grey Wolves. What is Metaheuristic? Metaheuristic means a High-level problem-independent algorithmic framework (develop optimization algorithms). Metaheuristic algorithms find the best solution out of all possible solutions of optimization. Who are the Grey Wolves? Wolf (Animal): Wolf Lived in a highly organized pack. Also known as Gray wolf or Grey Wolf, is a large canine. Wolf Speed is 50-60 km/h. Their Lifespan is 6-8 years (in the wild). Scientific Name: Canis Lupus. Family: Canidae (Biological family of dog-like carnivorans). Grey Wolves lived in a highly organized pack. The average pack size ranges from 5-12. 4 different ranks of wolves in a pack: Alpha Wolf, Beta Wolf, Delta Wolf, and Omega Wolf. How Grey Wolf Optimiza...
Whale Optimization Algorithm Code Implementation Whale Optimization Algorithm Code Files function obj_fun(test_fun) switch test_fun case 'F1' x = -100:2:100; y=x; case 'F2' x = -10:2:10; y=x; end end function [LB,UB,D,FitFun]=test_fun_info(C) switch C case 'F1' FitFun = @F1; LB = -100; UB = 100; D = 30; case 'F2' FitFun = @F2; LB = -10; UB = 10; D = 30; end % F1 Test Function function r = F1(x) r = sum(x.^2); end % F2 Test Function function r = F2(x) r = sum(abs(x))+prod(abs(x)); end end function Position = initialize(Pop_Size,D,UB,LB) SS_Bo...
Grey Wolf Optimization Algorithm Numerical Example Grey Wolf Optimization Algorithm Steps 1.) Initialize Grey Wolf Population. 2.) Initialize a, A, and C. 3.) Calculate the fitness of each search agent. 4.) šæ_š¶ = best search agent 5.) šæ_š· = second-best search agent 6.) šæ_š¹ = third best search agent. 7.) while (t<Max number of iteration) 8.) For each search agent update the position of the current search agent by the above equations end for 9.) update a, A, and C 10.) Calculate the fitness of all search agents. 11.) update šæ_š¶, šæ_š·, šæ_š¹ 12.) t = t+1 end while 13.) return šæ_š¶ Grey Wolf Optimization Algorithm Numerical Example STEP 1. Initialize the Grey wolf Population [Initial Position for each Search Agent] š_(š ) (i = 1,2,3,ā¦n) n = 6 // Number of Search Agents [ -100, 100] // Range Initial Wolf Position 3.2228 4.1553 -3.8197 4.2330 ...
There are about 1000 species of Bats. Bat Algorithm is based on the echolocation behavior of Micro Bats with varying pulse rates of emission and loudness. All bats use echolocation to sense distance and background barriers. Microbats are small to medium-sized flying mammals. Micro Bats used a Sonar that is known as Echolocation to detect their prey. Bats fly randomly with the velocity at the position with a fixed frequency and loudness for prey. Q. Whats is Frequency? A. Frequency is the number of waves that pass a fixed point in unit time. Wavelength is the minimum distance between two nearest particles which are in the same phase. Here, Sound waves are used by microbats to detect prey. Q. What is Position? A. A place where something or someone is located. Q. What is Velocity? A. Speed of something in a given direction. Q. What is loudness. A. Loudness refers to how soft or loud sound seems to listeners. Q. What is pulse rate? ...
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