Ant Colony Optimization for the Ship Berthing Problem Chia Jim Tong, Hoong Chuin Lau, and Andrew Lim School of Computing National University of Singapore Lower Kent Ridge Road Singapore 119260 chia@acm.org, lauhc@comp.nus.edu.sg, alim@comp.nus.edu.sg Abstract. Ant Colony Optimization (ACO) is a paradigm that em- ploys a set of cooperating agents to solve functions or obtain good so- lutions for combinatorial optimization problems. It has previously been applied to the TSP and QAP with encouraging results that demonstrate its potential. In this paper, we present FF-AS-SBP, an algorithm that applies ACO to the ship berthing problem (SBP), a generalization of the dynamic storage allocation problem (DSA), which is NP-complete. FF-AS-SBP is compared against a randomized first-fit algorithm. Ex- perimental results suggest that ACO can be applied effectively to find good solutions for SBPs, with mean costs of solutions obtained in the experiment on difficult (compact) cases ranging from 0% to 17% of opti- mum. By distributing the agents over multiple processors, applying local search methods, optimizing numerical parameters and varying the basic algorithm, performance could be further improved. 1 Ant Colony Optimization The Ant Colony Optimization (ACO) paradigm was introduced in [1], [2] and [3] by Dorigo, Maniezzo and Colorni. ACO has been applied effectively to the traveling salesman problem (TSP) [4] and the quadratic assignment problem (QAP) [5], among several other problems. The basic idea of ACO is inspired by the way ants explore their environment in search of a food source, wherein the basic action of each ant is: to deposit a trail of pheromone (a kind of chemical) on the ground as it moves, and to probabilistically prefer moving in directions with high concentrations of pheromone deposit. As an ant moves, the pheromone it leaves on the ground marks the path that it takes. Another ant that passes by later can detect the pheromone and decide to follow the trail with high probability. If it does follow the trail, it leaves its own pheromone on it, thus reinforcing the existing pheromone deposit. By this mechanism, the movement of ants along a path between the nest and the food reinforces the pheromone deposit on it, and this in turn encourages further traffic along the path. This behavior characterized by positive feedback is described as autocatalytic. P.S. Thiagarajan, R. Yap (Eds.): ASIAN’99, LNCS 1742, pp. 359–370, 1999. c  Springer-Verlag Berlin Heidelberg 1999