A Simplified Artificial Life Model for Multiobjective Optimisation:\ A Preliminary Report

Recent research in the field of Multiobjective\ Optimisation (MOO) has been focused on achieving the\ Pareto optimal front by explicitly analysing the\ dominance level of individual solutions. While such\ approaches have produced good results for a variety of\ problems, they are computationally expensive due to the\ complexities of deriving the dominance level for each\ solution against the entire population. TB_MOO\ (Threshold Based Multiobjective Optimisation) is a new\ artificial life approach to MOO problems that does not\ analyse dominance, nor perform any agent-agent\ comparisons. This reduction in complexity results in a\ significant decrease in processing overhead. Results\ show that TB_MOO performs comparably, and often\ better, than its more complicated counter-parts with\ respect to distance from the Pareto optimal front, but is\ slightly weaker in terms of distribution and extent.