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Poplar Optimization Algorithm || Step-By-Step || ~xRay Pixy

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The Poplar Optimization Algorithm (POA) is a nature-inspired optimization method based on how poplar trees reproduce. It uses sexual propagation (seed dispersal by wind) for exploration and asexual reproduction (cutting and regrowth) for exploitation. Mutation and chaos factors help maintain diversity and prevent premature convergence, making POA efficient for solving complex optimization problems. Learn the Poplar Optimization Algorithm Step-By-Step using Examples. Video Chapters: Poplar Optimization Algorithm (POA) 00:00 Introduction 02:12 POA Applications 03:32 POA Steps 05:50 Execute Algorithm 1 13:45 Execute Algorithm 2 16:38 Execute Algorithm 3 18:15 Conclusion Main Points of the Poplar Optimization Algorithm (POA) Nature-Inspired Algorithm ā€“ Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) ā€“ Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) ā€“ Strong branches grow ...
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Software Engineering

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 Software Engineering 

Software Engineering: Software Engineering (S.E.) is a profession dedicated to the designs, implementation, and modification of software. Applications of Software Engineering are: 
  1. Re-engineering of software
  2. Software Testing
  3. Software Maintainance 
  4. Software Analysis
  5. Software Design
  6. Software Implementation
The objective of software engineering is to produce good quality software, on time and within budget. To obtain this objective it is very important to focus on Software Quality and Software Development Process.
Software Characteristics are: 
  1. Reusability of the components.
  2. Softwares are not manufactured as hardware. 
  3. In the Software development process, there is no wear-out phase.
  4. Software is fixable.
Software Life Cycle Models: Software life cycle means the time period when a software product is conceived and when the software product is no longer available for use. The software Life cycle includes different phases:
  1. Requirement Phase
  2. Design Phase
  3. Implementation Phase
  4. Testing Phase
  5. Installation Phase
  6. Operation and Maintenance Phase
  7. Retirement Phase 
Software Requirement - > Software Design - > Software Implementation - > Software Testing - > Software Installation - > Software Maintenance - > Software Retirement. 

Software life cycle models in software engineering are often known as Software Development Life cycle (S.D.L.C). Different software life cycle models are:
  1. The Waterfall Model
  2. Increment Process Models
  3. Evolutionary Process Model
  4. Prototyping Model
  5. Spiral Model
  6. COCOMO Model - Constructive Cost Estimation Model
COCOMO Model: COCOMO (Constructive Cost Estimation Model) is a heuristic estimation technique proposed by Boehm. According to Bohem, there are 3 categories for software development based on development complexity: 
  1. Organic - Application. 
  2. Semidetached - Utility.
  3. Embedded - System Programs.
Software cost estimation should be done through 3 stages: Basic COCOMO, Intermediate COCOMO, and Complete COCOMO. COCOMO models are useful for project Time and Cost estimation. 







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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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 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=āˆ’1.2 2.) Fitness Function u...
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Cuckoo Search Algorithm for Optimization Problems

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Particle Swarm Optimization (PSO)

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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 ...
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PSO (Particle Swarm Optimization) Example Step-by-Step

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 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...
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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Particle Swarm Optimization Implementation in Python Video Chapters: 00:00 Introduction 02:01 Code 05:55 Position Initialization 08:06 PSO Main Loop 08:42 Velocity Calculation 10:02 Position Update 10:36 Fitness Evaluation 13:21 Objective Function 17:44 Result 19:00 Conclusion .....................................................SOURCE CODE......................................................................... import random import numpy as np from tkinter import messagebox #Define Class Particles class Particle: def __init__ (self,position): self.position=position self.velocity=np.zeros_like(position) self.best_position=position self.best_fitness=float('inf') def PSO(ObjF,Pop_Size,D,MaxT): swarm_best_position=None swarm_best_fitness=float('inf') particles=[] #Posotion Initialization position=np.random.uniform(-0.5,0.5,D) particle=Particle(position) particles.append(particle) #Fit...
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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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Grey Wolf Optimization Algorithm

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 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...
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Grey Wolf Optimization Algorithm Numerical Example

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