An Improved Nonlinear Tuna Swarm Optimization Algorithm Based on Circle Chaos Map and Levy Flight Operator

Abstract

The tuna swarm optimization algorithm (TSO) is a new heuristic algorithm proposed by observing the foraging behavior of tuna populations. The advantages of TSO are a simple structure and fewer parameters. Although TSO converges faster than some classical meta-heuristics algorithms, it can still be further accelerated. When TSO solves complex and challenging problems, it often easily falls into local optima. To overcome the above issue, this article proposed an improved nonlinear tuna swarm optimization algorithm based on Circle chaos map and levy flight operator (CLTSO). In order to compare it with some advanced heuristic algorithms, the performance of CLTSO is tested with unimodal functions, multimodal functions, and some CEC2014 benchmark functions. The test results of these benchmark functions are statistically analyzed using Wilcoxon, Friedman test, and MAE analysis. The experimental results and statistical analysis results indicate that CLTSO is more competitive than other advanced algorithms. Finally, this paper uses CLTSO to optimize a BP neural network in the field of artificial intelligence. A CLTSO-BP neural network model is proposed. Three popular datasets from the UCI Machine Learning and Intelligent System Center are selected to test the classification performance of the new model. The comparison result indicates that the new model has higher classification accuracy than the original BP model.