Paper Info
Title
A Geometric Structure-Based Particle Swarm Optimization Algorithm for Multiobjective Problems
Abstract
This paper presents a novel evolutionary strategy for multiobjective optimization in which a population's evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence.
Year
DOI
Venue
2017
10.1109/TSMC.2016.2523938
IEEE Trans. Systems, Man, and Cybernetics: Systems
Keywords
Field
DocType
Splines (mathematics),Surface topography,Surface reconstruction,Optimization,Mathematical model,Sociology,Statistics
Particle swarm optimization,Population,Mathematical optimization,Computer science,Meta-optimization,Algorithm,Multi-swarm optimization,Multi-objective optimization,Evolution strategy,Local search (optimization),Optimization problem
Journal
Volume
Issue
ISSN
PP
99
2168-2216
Citations 
PageRank 
References 
5
0.39
12
Authors
4
Name
Order
Citations
PageRank
Wenqiang Yuan1142.12
Yusheng Liu219535.64
hongwei wang3368.68
Yanlong Cao4294.08