Open AccessThe Hybrid Intelligent Optimization Algorithm and Multi-objective Optimization Based on Big Data
Author(s) -
Xin Nie,
Jian Luo
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012132
Subject(s) - multi swarm optimization , meta optimization , mathematical optimization , particle swarm optimization , metaheuristic , imperialist competitive algorithm , multi objective optimization , derivative free optimization , optimization problem , computer science , engineering optimization , evolutionary algorithm , swarm intelligence , test functions for optimization , pareto principle , mathematics
Multi objective optimization problem (MOP) usually has more than two objective functions, and the optimal solution based on Pareto frontier is obtained. The traditional optimization algorithm cannot meet the needs of industrial application when dealing with multi-objective optimization problems. With the good performance of evolutionary algorithm in solving complex problems, its application field is also extended to multi-objective optimization problems. The Pareto optimal solution and evaluation system of multi-objective optimization problem are analysed. Particle swarm optimization (PSO), one of the evolutionary algorithms based on swarm intelligence, is briefly introduced. The combination of particle swarm optimization algorithm and multi-objective optimization is studied.