Multi-State Particle Swarm Optimization for Discrete Combinatorial Optimization Problem
The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are proposed to solve discrete optimization problems. Many works have focused on the improvement of the binary-based algorithms. Yet, none of these works have been represented in states. In this paper, by...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
United Kingdom Simulation Society
2014
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/6412/ http://umpir.ump.edu.my/id/eprint/6412/ http://umpir.ump.edu.my/id/eprint/6412/1/Multi-State_Particle_Swarm_Optimization_for_Discrete_Combinatorial_Optimization_Problem.pdf |
| Summary: | The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are proposed to solve
discrete optimization problems. Many works have focused on the improvement of the binary-based algorithms. Yet, none of these works have been represented in states. In this paper, by implementing the representation of state in particle swarm optimization (PSO), a variant of PSO called multi-state particle swarm optimization (MSPSO) algorithm is proposed. The proposed algorithm works based on a simplified mechanism of transition between two states. The performance of MSPSO algorithm is emperically compared to BPSO and other two binary-based algorithms on six sets of selected benchmarks instances of traveling salesman problem (TSP). The experimental results showed that the newly introduced approach manage to obtain comparable results, compared to other algorithms in consideration. |
|---|