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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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Operator Precedence & Operators Associativity

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Operator Precedence  in C++
Operator precedence determines which operator is performed first in an expression with more than one operator.  In C++, we use the Operator Precedence rule to solve the problem like this- (5 + 8 * 3). Precedence Determine the order in which the operators are performed. An operator with Higher Precedence is evaluated before the operator with lower precedence.

https://youtu.be/5_3IN1NHa9k
Video: Operator Precedence in C++


Operator Associativity Chart


Multiply (*) operator has higher precedence than addition operator (+).


Questions for your Practice
1.) 7 + 3 * 2                                             Ans.  13
2.) 10 + 20 * 30 Ans.  610
3.) 100 / 10 * 10 Ans.  100
4.) 100 + 200 / 10 ā€“ 3 * 10                Ans.  90
5. ) 10 / 5 + 6 ā€“ 5 % 3 * 5        Ans.  - 2



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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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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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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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

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

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Grey Wolf Optimization Algorithm Numerical Example

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