Proposed modified computational model for the amoeba-inspired combinatorial optimization machine
This work addresses the challenge of enhancing bio-inspired optimization algorithms for researchers in computational biology and optimization, though it appears incremental as it builds on a previously proposed model.
The authors tackled the problem of improving the solution quality of an amoeba-inspired combinatorial optimization machine by modifying its computational model, finding that appropriate modifications significantly enhance performance and that volume conservation is not indispensable as previously believed.
A single-celled amoeba can solve the traveling salesman problem through its shape-changing dynamics. In this paper, we examine roles of several elements in a previously proposed computational model of the solution-search process of amoeba and three modifications towards enhancing the solution-search preformance. We find that appropriate modifications can indeed significantly improve the quality of solutions. It is also found that a condition associated with the volume conservation can also be modified in contrast to the naive belief that it is indispensable for the solution-search ability of amoeba. A proposed modified model shows much better performance.