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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.
🔻YouTube: https://youtu.be/XvQw0RALeTo
🔹Telegram:
🆔 @MATLAB_House
#BSO #algorithm #heuristic #optimization #search_space #bacteria #population #exploration #exploitation
@MATLABHOUSE
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.
🔻YouTube: https://youtu.be/XvQw0RALeTo
🔹Telegram:
🆔 @MATLAB_House
#BSO #algorithm #heuristic #optimization #search_space #bacteria #population #exploration #exploitation
@MATLABHOUSE
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