A self-adaptive discrete particle swarm algorithm is proposed.In order to overcome the premature convergence of the algorithm

a repulsive process is introduced to increase the swarm diversity and a metric to measure the swarm diversity is also designed.The attractive and repulsive processes can adaptively change during running.To speed up convergence

a strategy used to control the inertia weight is advanced which changes dynamically with the iterations during different running phrase of the algorithm.Moreover

algorithm performance can be enhanced further if local search strategies are combined.Finally

the proposed algorithm is used to solve the TSP and FSSP problems and compared with other related algorithms.The experiment results showed its superiority.