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❇️Bacterial Foraging Optimization (BSO) ❇️

The text describes a 2D optimization problem aiming to minimize the distance between a position (x1, x2) and the target point (1, 2), with the optimal solution being (1, 2) where the fitness value is zero. It introduces the Bacterial Swarm Optimization (BSO) algorithm, a heuristic method inspired by bacterial foraging behavior. The algorithm operates through a population of individuals that navigate the search space to find the optimal solution based on fitness values and probabilistic rules. It adapts step size and swim length for a balance between exploration and exploitation, and uses elimination-dispersal events to avoid local optima. The algorithm's effectiveness depends on parameter selection and the problem's nature.
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#BSO #algorithm #heuristic #optimization #search_space #bacteria #population #exploration #exploitation
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Genetic Algorithm Optimization in MATLAB: Visualizing Fitness Progression

In this video, we showcase the implementation of a Genetic Algorithm (GA) optimization technique using MATLAB. The GA is applied to optimize a 2-variable function by iteratively evolving a population of candidate solutions. The video demonstrates the fitness progression over generations, with the best, worst, and average fitness values plotted. The algorithm incorporates selection, crossover, and mutation operations to drive the evolution of the population. The function landscape, population, and elite individuals are visualized using contour plots and scatter points. Watch this video to gain insights into how a GA can be utilized for optimization tasks and witness the evolution of the population towards finding optimal solutions.

YouTube: https://youtu.be/SJ1zXyEbl0M

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#GeneticAlgorithm #Optimization #MATLAB #FitnessProgression #EvolutionaryAlgorithms #AlgorithmVisualization

In this MATLAB programming example, we solve an optimal control problem using the Pontryagin's Maximum Principle. We use the state equations, cost function, Hamiltonian, and costate equations to obtain the optimal control. The solution is obtained using the "dsolve" function, and the results are visualized using MATLAB plots. This example is taken from the "Crack Optimal Control" book.

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https://www.youtube.com/watch?v=eN9qZ-dOskM

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#OptimalControl #MATLAB #PontryaginMaximumPrinciple #CrackOptimalControl #StateEquations #CostFunction #Hamiltonian #CostateEquations #dsolve #MATLABPlots #ControlTheory #Optimization #DynamicProgramming #NumericalMethods #Engineering #Mathematics #Coding #Programming #ArtificialIntelligence #MachineLearning #SymbolicComputation #Visualization #Simulation #MathModeling #Education #STEM