Convergence and Diversity Evaluation for Vector Evaluated Particle Swarm Optimization
Multi-objective optimization can be commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm has increasing popularity in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO)...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/5676/ http://umpir.ump.edu.my/id/eprint/5676/1/Paper_ICMIC_Published_Zaidi.pdf |
| Summary: | Multi-objective optimization can be commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm has increasing popularity in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. However, VEPSO quantitative performance measure has not been investigated.
Hence, in this study, the performance of VEPSO algorithm is
investigated by measuring the convergence and diversity by
using standard test functions. In addition, comparisons with
other optimization algorithms are also conducted. The results show that the VEPSO algorithm performs weakly in solving problems with concave, mixed, and disconnected Pareto frontier and performs badly in solving multi-modal problems.
|
|---|