A Multi-objective Particle Swarm Optimization using Logistics-tent Chaotic Map with GOBL and Non-inertial Levy Flight

To address the slow learning of traditional PSO algorithms and alleviate population collapse in the objective space, this paper proposes a multi-objective particle swarm optimization algorithm using a Logistic-tent chaotic map with GOBL and non-inertial Lévy flight (MOPSOLGN). Firstly, the Logistic-tent chaotic map with generalized opposition-based learning initializes individual positions, avoiding blindness and uncertainty in the initial population and improving its distribution. Secondly, a particle flight method combining an individual competition mechanism with k-means clustering divides particles into losers and winners, using a new non-inertial Lévy flight dynamical equation to balance exploration and exploitation and ensure the algorithm can escape local solutions. Thirdly, a differential mutation strategy enables the population to escape collapse and increases the diversity of the optimal solution set. Comparative experiments with state-of-the-art multi-objective algorithms on benchmark functions verify that MOPSOLGN allows individuals to converge to the real Pareto frontier more quickly and with better distribution.