Non-linear Random Change Differential Evolution for Multi-objective Resource Allocation Problem

Abstract

The resource allocation problem (RAP) has a wide range of applications in the transportation engineering. The differential evolution algorithm proposed to solve Chebyshev’s inequality is essentially an optimization algorithm for the most optimal solution. However, the basic differential evolution algorithm tends to fall into the local optimal solution, resulting in premature convergence of the population, especially in multi-objective resource allocation of transportation engineering. To address these problems, a Non-linear Random Change Differential Evolution was proposed to solve multi-objective resource allocation problems. With tests to the benchmark functions in CEC, the results show that both the non-linear processing and the random changing after the mutation make the result closer to the optimal value. The method make it possible to solve the NP problem of multi-objective allocation in transportation engineering.