Improved salp swarm algorithm for solving single-objective optimization problems
Sea Cucumber Swarm Algorithm (SCSA) is an optimization algorithm based on swarm intelligence, which is inspired by the behavior of natural salp groups. It simulates the behavior of salps when searching for food and escaping from danger, and solves single-objective optimization problems by optimizing search strategies. This article will introduce an improved salp swarm algorithm that introduces attenuation factors and dynamic learning strategies to improve search capabilities.
Algorithm Description:
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Initialization phase:
- Set parameters such as population size, number of iterations, attenuation factor, and learning rate.
- Randomly generate initial salp individuals and calculate their fitness values.
- Initialize the global optimal salp individual and fitness.
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Iterative search phase:
- For each salp individual, the individual speed and position are updated based on its fitness value and the fitness value of the global optimal salp individual.
- Adjust the learning rate according to the dynamic learning strategy.
- Gradually reduce the individual speed according to the attenuation factor.
- Update the fitness value of the individual, and update the global optimal salp individual and fitness.
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Termination condition judgment:
- Reach the specified number of iterations.
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Output result:
- Output the global optimal salp individual and fitness.
MATLAB code implementation:
% 参数设置
populationSize = 50