Approximation Algorithms for Dynamic Storage Allocations
ESA '96 Proceedings of the Fourth Annual European Symposium on Algorithms
An effective ship berthing algorithm
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ant Colony Optimisation solution to distribution transformer planning problem
International Journal of Advanced Intelligence Paradigms
Particle swarm optimization algorithm for the berth allocation problem
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
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.