Aiming at the difficulty of searching Pareto front for complex multi-objective optimization

a Memetic learning strategy which combines many of population preying-competition mechanism with reinforcement learning mechanism and evolutionary computation model was designed.Inspired by ecological principle

such as the population evolution and the competition between predator populations and prey populations

a multi-objective Memtic optimization algorithm (multi-objective Memetic algorithm based on ecological population preying-competition model

ECPM-MOMA) was proposed.In ECPM-MOMA

Preying-competition and Reinforcement Learning operator was designed and applied for global search.Memetic search operator was also applied for local search in the population evolution process.Experimental results show that the proposed algorithm has better convergence performance and distribution characteristics.The ecological preying-competition strategy and evolutionary computation model is effective for solving complex multi-objective optimization problems.