PSOt: A Comprehensive Particle Swarm Optimization Toolbox

PSOt serves as a specialized Particle Swarm Optimization toolbox that encapsulates the core PSO algorithm components while exposing adjustable parameters to users. The implementation requires users to define their objective function (for either minimization or maximization calculations), set variable boundaries, and configure iteration constraints such as maximum velocity (Max_V) to initiate autonomous optimization.

When using PSOt, users can fine-tune algorithm parameters including learning factors and inertia weights through intuitive function interfaces. The toolbox incorporates multiple heuristic enhancements such as adaptive inertia weight reduction and local search strategies, implemented through modular functions that improve convergence efficiency for complex optimization landscapes.

PSOt supports parallel computing capabilities through MATLAB's Parallel Computing Toolbox integration, enabling acceleration via multi-core processors or distributed computing environments. Users can select appropriate parallelization methods through configuration flags based on available computational resources.

In summary, PSOt provides a robust and flexible optimization framework featuring customizable algorithm parameters and parallel processing support. The toolbox delivers effective solutions for both simple unimodal and complex multimodal optimization problems through its implementation of standard PSO variants with extensible architecture.