An Immune Evolutionary Algorithm with Punishment Mechanism for Public Procurement Expert Selection

In the past decade, fairness in public procurement expert selection has attracted research attention. This paper proposes an immune evolutionary algorithm (IEA) with a punishment mechanism for expert selection, in which an ordered weighted aggregation (OWA) operator is applied to adjust the score weights to reduce expert evaluation committee abuse discretion and Grubbs method is employed to test the outliers. The results from a real-life public procurement case demonstrated that the abnormal experts could be effectively suppressed during the selection process and that the proposed method performed better than either the random selection algorithm or IEA, neither of which considers a punishment mechanism. Therefore, the proposed method, which applied the abnormal data detected in the scoring process to the expert selection process with a punishment mechanism, was proven to be effective in solving public procurement problems that may have doubtful or abnormal experts.

Monkey King Immune Evolutionary Algorithm

Monkey-King genetic algorithm has the shortages of the lower searching ability in the local area and further in the whole area at monkey-king point in spite of the advantages of the simple principle and easy calculation. Monkey-king point was optimized iteratively by using immune evolutionary algorithm. This method overcomes the premature convergence because of the optimal searching in the out as well as in of the monkey-king point. At the same time, with the lapse of iteration, the algorithm closes in the whole of optimal solution with the higher precision because of the gradual strengthening of local searching ability. Calculation and comparison with several methods, such as monkey-king genetic algorithm, improved monkey-king genetic algorithm and common climbing operator genetic algorithm et al, has made. The results show that the monkey-king immune evolutionary algorithm has the optimal searching ability and the stability all the better.