Ant Colony Optimization for the Ship Berthing Problem

Abstract. Ant Colony Optimization (ACO) is a paradigm that employs a set of cooperating agents to solve functions or obtain good solutions 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. Experimental 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 optimum. 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 Optimiz...

• Type: Conference
• Year: 1999
• Where: ASIAN
• Authors: Chia Jim Tong, Hoong Chuin Lau, Andrew Lim
• Keywords: Algorithms, Ant Colony Optimization, ASIAN 1999, Pheromone, Pheromone Deposit