A Hybrid Differential Evolution Algorithm – Game Theory for the Berth Allocation Problem

Abstract

The berth allocation problem (BAP) is an important and challenging problem in the maritime transportation industry. BAP can be defined as the problem of assigning a berth position and service time to a given set of vessels while ensuring that all BAP constraints are respected. The goal is to minimize the total waiting time of all vessels. In this paper, we propose a differential evolution (DE) algorithm for the BAP. DE is a nature-inspired meta-heuristic that has been shown to be an effective method to addresses continuous optimization problems. It involves a population of solutions that undergo the process of selection and variation. In DE, the mutation operator is considered the main variation operator responsible for generating new solutions. Several mutation operators have been proposed and they have shown that different operators are more suitable for different problem instances and even different stages in the search process. In this paper, we propose an enhanced DE that utilizes several mutation operators and employs game theory to control the selection of mutation operators during the search process. The BAP benchmark instances that have been used by other researchers are used to assess the performance of the proposed algorithm. Our experimental results reveal that the proposed DE can obtain competitive results with less computational time compared to existing algorithms for all tested problem instances.