Back to nature: Improving MOPSO inspired by the behaviour of starlings

Abstract

The canonical MOPSO algorithm was adapted to include behaviour observed in starling flocks. An observer can see the amazing aerial display of large starling flocks. They maintain uniformity and cohesion throughout flight and landing. This behaviour emerges from the individuals following a set of simple rules governing motion and interaction. The adaption to the canonical MOPSO was done by extracting these rules to provide the algorithm with behaviour that improved uniformity and spread of the archived solutions. The adapted MOPSO was applied to ZDT1 - ZDT4. There was significant improvement in uniformity and spreading of the final archive solutions. The improvement in coverage was as high as 25.4% in the case of ZDT4. There was also an improvement in spread: ZDT1 by a factor of 8.4, ZDT2 by a factor of 4.78, ZDT3 by a factor of 1.6, and ZDT4 by a factor of 3.76. Local search was then added to the algorithm. The convergence showed significant improvement without loss of the newly improved coverage and spread. With better understanding of how and why behaviour emerges, we were able to improve the canonical MOPSO by adapting its fundamental rules leading to emergent behaviour that intrinsically improved deficiencies in uniformity and spread of archive solutions.