Multi-objective feasibility enhanced particle swarm optimization

Date

2018-12-02

Author

Hasanoglu, Mehmet Sinan
Dölen, Melik

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

16
views

0
downloads

Cite This

This article introduces a new method entitled multi-objective feasibility enhanced partical swarm optimization (MOFEPSO), to handle highly-constrained multi-objective optimization problems. MOFEPSO, which is based on the particle swarm optimization technique, employs repositories of non-dominated and feasible positions (or solutions) to guide feasible particle flight. Unlike its counterparts, MOFEPSO does not require any feasible solutions in the initialized swarm. Additionally, objective functions are not assessed for infeasible particles. Such particles can only fly along sensitive directions, and particles are not allowed to move to a position where any previously satisfied constraints become violated. These unique features help MOFEPSO gradually increase the overall feasibility of the swarm and to finally attain the optimal solution. In this study, multi-objective versions of a classical gear-train optimization problem are also described. For the given problems, the article comparatively evaluates the performance of MOFEPSO against several popular optimization algorithms found in the literature.

Subject Keywords

Management Science and Operations Research, Industrial and Manufacturing Engineering, Control and Optimization, Applied Mathematics, Computer Science Applications

URI

https://hdl.handle.net/11511/42570

Collections

Department of Mechanical Engineering, Article