The biggest leaps forward in technology innovation sometimes require setting one step back and taking a cue from the greatest of all innovators: Mother Nature. Some of the most effective methods of design optimization in man-made technology are derived from naturally occurring processes. Particle Swarm Optimization (PSO), a population based stochastic optimization technique, is one such method. The algorithm is inspired by social behavior typically observed in schools of fish, flocks of birds, or ant colonies. All of those provide good examples of naturally-occurring swarming behavior which provides the basis for this optimization method originally developed in 1995 by Dr. Eberhart and Dr. Kennedy.

In nature, swarm intelligence as seen with birds, fish and insects alike, is created by each individual within a group using its own experience and the experience of its neighbors to choose how to move next. All individuals within the flock are continuously adjusting their positions by making compromises between the ‘local best positions’ pursued by each of them individually and the ‘global best positions’ indicated by the entire flock. In essence, what is good for the flock is good for the individual - and because all of them are smart enough to understand this, they work together toward the efficiency of the whole.

The new Particle Swarm Optimization (PSO) algorithm that we have made available in Optimus, implements this swarm intelligence approach into an industry-proof method. It can consistently be used to solve both single-objective and multi-objective optimization challenges.

Here are 3 good reasons why you should look at this industrial PSO implementation available in Optimus:

• A real time saver: Optimus’ PSO algorithm efficiently handles high-dimensional optimization challenges, and allows for parallel execution of experiments. As it matches results of existing genetic and evolutionary optimization algorithms using far less experiments, it significantly cuts on processing time.
• Highly accurate: not only does Optimus’ Particle Swarm Optimization algorithm deliver highly accurate optimal design points (Pareto front), it also delivers those design points in a robust way (no matter how you specify the initial parameters that the algorithm should use).
• Incredibly easy to use: we have done a lot of work to make sure that you do not need to be an optimization guru to benefit from this algorithm. The result is an easy-to-use and robust solution offering a consistent user experience for both single-objective and multi-objective design optimization challenges.

Want to learn more about Optimus’ PSO implementation and how it can be applied to your design optimization challenges? Send us an email ([email protected])!