| Title | An Improved Multi-objective Particle Swarm Optimization |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Xu, S., Ouyang, Z., Feng, J. |
| Conference Name | 2020 5th International Conference on Computational Intelligence and Applications (ICCIA) |
| Date Published | jun |
| Keywords | Approximation algorithms, composability, compositionality, convergence, evolutionary computation, genetic algorithms, MOEA/D, MOPSO, multi-objective optimization algorithm, multi-objective particle swarm optimization, multiobjective evolutionary algorithm based on decomposition, multiobjective particle swarm optimization, nondominated sorting genetic algorithm II, NSGA-II, Optimization, Pareto optimisation, particle swarm optimisation, particle swarm optimization, pubcrawl, Sociology, Sorting, Statistics, swarm intelligence, track planning problems, ZDT\textbackslashtextbackslashDTLZ test functions |
| Abstract | For solving multi-objective optimization problems, this paper firstly combines a multi-objective evolutionary algorithm based on decomposition (MOEA/D) with good convergence and non-dominated sorting genetic algorithm II (NSGA-II) with good distribution to construct. Thus we propose a hybrid multi-objective optimization solving algorithm. Then, we consider that the population diversity needs to be improved while applying multi-objective particle swarm optimization (MOPSO) to solve the multi-objective optimization problems and an improved MOPSO algorithm is proposed. We give the distance function between the individual and the population, and the individual with the largest distance is selected as the global optimal individual to maintain population diversity. Finally, the simulation experiments are performed on the ZDT\textbackslashtextbackslashDTLZ test functions and track planning problems. The results indicate the better performance of the improved algorithms. |
| DOI | 10.1109/ICCIA49625.2020.00011 |
| Citation Key | xu_improved_2020 |
An Improved Multi-objective Particle Swarm Optimization