A Multi-Objective Multi-Colony Ant Algorithm For Solving The Berth Allocation Problem

This paper considers the allocation of a fixed number of berths to a number of ships arriving at the port within the planning horizon for container handling by determining the berthing time and location, in terms of berth index, for each ship. The solution to this berth allocation problem (BAP) involves the optimization of complete schedules with minimum service time and delay in the departure of ships, subject to a number of temporal and spatial constraints. To solve such a multi-objective and multi-modal combinatorial optimization problem, this paper presents a multi-objective multi-colony ant algorithm (MOMCAA) which uses an island model with heterogeneous colonies. Each colony may be different from the other colonies in terms of the combination of pheromone matrix and visibility heuristic used. In contrast to conventional ant colony optimization (ACO) algorithms where each ant in the colony searches for a single solution, the MOMCAA uses an ant group to search for each candidate solution. Each ant in the group is responsible for the schedule of a particular berth in the solution.