No abstract

This paper puts forward a multi-objective hybrid difference optimization algorithm to solve multiobjective flow-shop scheduling problem (FSP). The hybrid algorithm inherits the merits of differential evolution vector operation, and makes dynamic adjustments to the search direction based on historical data. However, the basic differential evolution algorithm is prone to the local optimum trap, due to the low population diversity in the later stage of evolution. To solve the problem, a hybrid sampling strategy was introduced obtain the distribution information of solution sets and to design the mutation operator of differential evolution, thus improving the convergence of the hybrid algorithm. Finally, our algorithm was applied to solve FSPs through simulation. The simulation results show that our algorithm greatly outperformed the basic multi-objective evolutionary algorithm in convergence and distribution performance.

This paper studies the green single-machine scheduling problem that considers the delay cost and the energy consumption of manufacturing equipment and builds its integrated optimization model. The improved ant colony scheduling algorithm based on the Pareto solution set is used to solve this problem. By setting the heuristic information, state transition rules, and other core parameters reasonably, the performance of the algorithm is improved effectively. Finally, the model and the improved algorithm are verified by the simulation experiment of 10 benchmark cases.