Optimus 10.14 introduces a new Particle Swarm Optimization (PSO) algorithm that can be used for both single-objective and multi-objective optimization. The new PSO algorithm is inspired by swarm intelligence typically found in animal flocks, fish schools and ant colonies. All swarm individuals continuously adjust their positions by making compromises between the ‘local best positions’ pursued by the individuals and the ‘global best positions’ indicated by the entire flock.
The new PSO algorithm efficiently handles high-dimensional optimization challenges, supports parallel execution of experiments, and delivers a highly accurate optimal design point or Pareto front. When processing time is most critical, the PSO algorithm is able to match the results of existing genetic and evolutionary algorithms using far less experiments. Additionally, this new Optimus algorithm is extremely easy and robust to use, both for single-objective and multi-objective optimization applications.