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In order to improve the performance of BFO (bacterial foraging optimization) algorithm in multi-modal problems, the algorithm is optimized and improved in this paper. In the chemotaxis operation, the gradient accumulation variable swimming step size is realized, and the convergence speed of the algorithm is accelerated. In the reproduction operation, a new population competition mechanism is added, and a double-index sorting is realized, which improves the ability of the algorithm to jump out of the local optimum and the diversity of the population. In the migration operation, the vitality index of bacteria is added to dynamically affect the migration probability, ensure the survival probability of elite individuals, and further improve the global optimization ability of the algorithm. Finally, a multi-modal function is selected to test. Compared with the genetic algorithm, the improved BFO algorithm is better than the genetic algorithm in speed and quality.

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