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Asynchronous Particle Swarm Optimization (APSO) via MPI for shared memory and distributed systems.
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README.md

Repository purpose

This repository houses an Asynchronous Particle Swarm Optimization (APSO) implemented in C++ using non-blocking features of MPI for use in shared and distributed memory systems.

Approach Highlights

This APSO technique uses asynchronous message handling and a relaxation of the global best position to help ensure parallel scalability of the method while still expecting relatively good convergence. The swarm is partitioned into subsets where each partition resides on some process. Locally to a process, it will perform the typical PSO method but each process will maintain a global best position estimate that will be updated not only by local particles, but also via messages from other partitions.

The way it works is, the user can specify how often we care to check messages and send out what our current estimate is for the global best solution. When a local process decides to send out its global best solution estimate, it will choose a small constant sized random subset of the partitions to send the non-blocking message to. When those partitions receive the message, they will respond with their global best solution estimate, after first seeing if the estimate they received beats what they have stored locally. All the message managing is handled whenever the local swarm decides it cares. This randomized approach to spreading a partition's estimate ensures that gradually partitions will, in expectation, get the global best estimated location eventually. The local computations done on a process also allow for typical PSO results and convergence, implying this method should work fine even if there is a lag to getting the true global best solution across the whole distributed swarm.

Current Features

The software contained in this project benefits from C++11 and some C++14 features and a relatively modular design. The asynchronous swarm is templated in terms of the objective function you care to optimize, allowing for compile time flexibility. The software manages the asynchronous communication and optimization loop for you already, so ultimately you just need to specify an objective function.

Future Plans

This asynchronous PSO method was written recently (June 26th), so there has not been a lot of time to build out a lot of features. I would like to investigate some other topologies, perhaps even some other techniques for resolving particles that leave the hypercube search domain. I think it would be great to find a way to pass around particles if a given process is seeing it operates slower than some other process. I may consider

Sample Parallel Results

The below figure is for a set of test problems of increasing cost that get distributed across 1, 2, 4, 8, 16, 32, and 48 processes, respectively. All the problems tackle a simple quadratic form objective with different combinations of overall swarm size and number of iterations. The test problems are setup as:

  • P1: 480 particles and 1000000 iterations
  • P2: 4800 particles and 2000000 iterations
  • P3: 4800 particles and 4000000 iterations
  • P4: 9600 particles and 4000000 iterations

The speedup plot and strong scaling efficiency plot are shown below.

Strong Scaling Speed-up Strong Scaling Efficiency

The above figure shows that indeed the algorithm maintains a near ideal speed up and relatively good efficiency.

Contact Info

Email: choward1491@gmail.com

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