Abstract
Hyper-spheres instead of hyper-parallelepipeds for proximity areas, and adaptation of the swarm size as well as the relationships between the particles offer an elegant and powerful theoretical framework for design and analysis of autonomous adaptive search heuristics. This chapter describes an autonomous adaptive search heuristic known as TRIBES, which in its current formulation is biased toward solving problems defined as rational points, and the transformation processes involved in realizing a version for solving combinatorial optimization problems. We apply the TRIBES methodology to the well-known flow shop-scheduling problem, and report simulation results. To demonstrate its effectiveness, we compare the solutions of the TRIBES with other emerging optimization techniques. The results show that TRIBES is promising for solving combinatorial optimization problems, which are of significant importance in the manufacturing sector.
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Onwubolu, G.C. (2004). TRIBES application to the flow shop scheduling problem. In: New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39930-8_21
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DOI: https://doi.org/10.1007/978-3-540-39930-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05767-0
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