Presenter Information

Eric Koessler, SUNY GeneseoFollow

Submission Type

Presentation

Start Date

22-4-2020 12:00 AM

Abstract

We test three methods of hybridizing Particle Swarm Optimization (PSO) and Pattern Search (PS) to improve the global minima, speed, and robustness. All methods let PSO run first followed by PS. The first method lets PSO use a large number of particles for a limited number of iterations. The second method lets PSO run normally until tolerance is reached. The third method lets PSO run normally until the average particle distance from the global best location is within a threshold. Numerical results using non-differentiable test functions reveal that all three methods improve the global minima and robustness versus PSO, while the third method also improves speed versus PSO. The third hybrid method was also applied to a basin network optimization problem and outperformed PSO with filter method and Genetic Algorithm (GA) with implicit filtering.

Comments

Faculty Sponsor: Dr. Ahmad Almomani

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Apr 22nd, 12:00 AM

358— Hybridization of Particle Swam Optimization and Pattern Search Algorithms With Application

We test three methods of hybridizing Particle Swarm Optimization (PSO) and Pattern Search (PS) to improve the global minima, speed, and robustness. All methods let PSO run first followed by PS. The first method lets PSO use a large number of particles for a limited number of iterations. The second method lets PSO run normally until tolerance is reached. The third method lets PSO run normally until the average particle distance from the global best location is within a threshold. Numerical results using non-differentiable test functions reveal that all three methods improve the global minima and robustness versus PSO, while the third method also improves speed versus PSO. The third hybrid method was also applied to a basin network optimization problem and outperformed PSO with filter method and Genetic Algorithm (GA) with implicit filtering.

 

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