Name
Title | ||
|---|---|---|
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients |
Abstract | ||
|---|---|---|
In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. TV-MOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for 11 function optimization problems, using different performance measures. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.ins.2007.06.018 | Information Sciences |
Keywords | Field | DocType |
Multi-objective optimization,Pareto dominance,Particle Swarm Optimization | Particle swarm optimization,Continuous optimization,Derivative-free optimization,Mathematical optimization,Meta-optimization,Multi-swarm optimization,Multi-objective optimization,Artificial intelligence,Imperialist competitive algorithm,Mathematics,Machine learning,Metaheuristic | Journal |
Volume | Issue | ISSN |
177 | 22 | 0020-0255 |
Citations | PageRank | References |
165 | 7.96 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Praveen Kumar Tripathi | 1 | 179 | 11.83 |
| Sanghamitra Bandyopadhyay | 2 | 3977 | 222.92 |
| Sankar K. Pal | 3 | 6410 | 627.31 |