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.
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Tong, C.J., Lau, H.C., Lim, A. (1999). Ant Colony Optimization for the Ship Berthing Problem. In: Thiagarajan, P.S., Yap, R. (eds) Advances in Computing Science — ASIAN’99. ASIAN 1999. Lecture Notes in Computer Science, vol 1742. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46674-6_30
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DOI: https://doi.org/10.1007/3-540-46674-6_30
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