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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Grasshopper Optimization Algorithm (G.O.A.) Step-by-Step with Numerical ...
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Grasshopper Optimization Algorithm (G.O.A.)
Grasshoppers are also known as pests. They destroy fields and crop production. Grasshopper lifecycle contains Eggs, Nymph Phases, and Adult Grasshopper. Grasshopper Optimization Algorithm is a Nature-inspired swarm-based optimization algorithm. Grasshopper Optimization Algorithm (GOA) is inspired by the foraging and swarming behavior of grasshoppers in nature. The grasshopper optimization algorithm is basically inspired by the behavior of adult grasshoppers in nature. Adult grasshoppers can make sudden jumps and cover long-range as compare to nymphs.
This is the mathematical model used to represent grasshopper behavior in this algorithm :
š„_š = š_š + šŗ_š + š“_i
GrasshopperCurrentPosition = Social Interaction in the group + Force of gravity + Wind Direction.
Normally distributed random values are used in the grasshopper optimization algorithm for grasshopper random behavior in nature.
Grasshopper Optimization Algorithm Steps.
1.) Parameter Initialization.
2.) Population Initialization Phase.
3.) Compute Fitness Value for each grasshopper.
4.) Select the Best Solution Among All.
5.) Check While (CurrentIteration (t) < MaximumIteration (MaxT)).
6.) Normalize distance between grasshoppers in the range [1, 4].
7.) Update the position of the current grasshopper.
8.) Bring the Current grasshopper back if it goes outside boundaries.
9.) Update Current Best Solution if there any new Best solution.
10.) CurrentIteration = CurrentIteration + 1; // End While Loop
11.) Return Best Solution.
Grasshopper Optimization Algorithm Advantages.
Obtain better solution as compare to other metaheuristic algorithms
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