dhji@whu.edu.cn.

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.

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Currently, computational grid, service grid and data grid are becoming richer and more complex. Grid infrastructure composed of heterogeneous resource is widely distributed, workflow scheduling problem in grid environment described by directed acrylic graph (DAG) becomes an important and difficult problem. Meanwhile, the research on workflow scheduling problem in grid environment mainly focuses on the time and cost constrained optimization, but the key problems about stability, flexibility, security and load balancing aren't considered adequately. Aiming at these problems, we redefine parameters of quality of service (QoS) and the model of grid workflow scheduling, and put forward a rotary hybrid discrete particle swarm optimization (RHDPSO) algorithm, in which double extremums are disturbed by the method of random time sequence based on rotation discretization, to overcome premature convergence and local optimum. The simulation results show that the RHDPSO algorithm has fast convergence, high precision and strong robustness, and can effectively restrain premature convergence, compared with DPSO. The performance of our algorithm is very promising.