In order to mitigate the difficulty of balancing diversity and convergence in heuristic algorithm

this paper proposes an IF-memetic hybrid double particle swarm optimization (IFMHDPSO) based on intuitionistic fuzzy memetic framework and multi-attribute decision. There are two independent exploration and exploitation populations employing distributed strategies in which social reinforcement operator and collision rebound operator are proposed to improve diversity of algorithm and explore new areas in populations of exploration. Moreover

an intuitionistic fuzzy multi-attribute decision making is built up for comprehensively evaluating the solution space to get the potential global optimal solution area

which can guide the PSO (Particle Swarm Optimization) with Lamarckian mechanism to carry out the local search to achieve cooperation between populations under different strategies and reasonable allocation of computational resources. Compared with other 5 new evolutionary algorithms

IFMHDPSO is of better comprehensive optimization in 23 benchmark function test results.