Optimisation problems have traditionally been dealt with by techniques from the operations research community such as branch and bound, cutting planes and dynamic programming. However, these often intractable problems are increasingly being solved by meta-heuristic class algorithms. Among these, simulated annealing, tabu search and genetic algorithms have been popular. A set of tools based on biological and evolutionary paradigms such as bird swarms and ant colonies have recently emerged. This paper surveys the progress of these algorithms on benchmark and real world problems. In addition, the types of problems each technique is suitable for as well as possible hybrid systems, are discussed.
|Title of host publication||Proceedings of the Inaugural Workshop on Artificial Life: AL'01|
|Editors||Hussein A. Abbass|
|Publisher||University of New South Wales|
|Number of pages||11|
|Publication status||Published - 2001|
Hendtlass, T., & Randall, M. (2001). A Survey of Ant Colony and Particle Swarm Meta-heuristics and their Application to Discrete Optimisation Problems. In H. A. Abbass (Ed.), Proceedings of the Inaugural Workshop on Artificial Life: AL'01 (pp. 15-25). University of New South Wales.