Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm for Global Optimization

Dragonfly algorithm (DA) is a recently proposed optimization based on swarm intelligence, which has been successfully applied in function optimization, feature selection, parameter adjustment, etc. However, it fails to take individual optimal position into consideration but only relies population and 5 behaviours update position, leading low accuracy, slow convergence, local optima. To overcome these drawbacks, Tent Chaotic Map Population Classification Evolution Strategy-Based Algorithm (TPDA) proposed. chaotic map used initialize the population, making individuals distributed more uniformly search space improve diversity efficiency. classified according fitness value, different methods are adopted for types of guide process ability TPDA jump out optima, thus realizing balance between exploration exploitation. The efficiency validated by tests 18 basic unconstrained benchmark functions. A comparative performance analysis TPDA, Particle Swarm Optimization (PSO), DA, Adaptive Learning Factor Differential Evolution-Based (ADDA) carried out. Experimental statistical results demonstrate that gives significantly better performances compared with PSO, ADDA average standard deviation all global capability high-dimensional functions comparison time complexity other intelligence algorithms also verified paper. indicate able perform optimizing without consuming computational time.