A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

Abstract

Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best particles, which is not so effective in high-dimensional objective space. To better solve MaOPs, this paper presents a novel angular-guided particle swarm optimizer (called AGPSO). A novel velocity update strategy is designed in AGPSO, which aims to enhance the search intensity around the particles selected based on their angular distances. Using an external archive, the local best particles are selected from the surrounding particles with the best convergence, while the global best particles are chosen from the top 20% particles with the better convergence among the entire particle swarm. Moreover, an angular-guided archive update strategy is proposed in AGPSO, which maintains a consistent population with balanceable convergence and diversity. To evaluate the performance of AGPSO, the WFG and MaF test suites with 5 to 10 objectives are adopted. The experimental results indicate that AGPSO shows the superior performance over four current MOPSOs (SMPSO, dMOPSO, NMPSO, and MaPSO) and four competitive evolutionary algorithms (VaEA, θ-DEA, MOEA\D-DD, and SPEA2-SDE), when solving most of the test problems used.

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

“…In addition, a new PSO variant (called CDLS/PSO) is further presented for solving the WFG1-WFG9 test problems [28], the WFG41-WFG48 test problems [29] and the DTLZ1-DTLZ7 test problems [30] with 5 to 15 dimensions, in which a novel efficient space-division-based archive update strategy is also implemented to coordinate with the proposed CDLS strategy. The experimental results validate the superior performance of PSO/CDLS over several state-of-the-art competitors, including three competitive PSOs (i.e., MPSO/D [26], AGPSO [31] and MaPSO [15]) and three promising EAs for solving MaOPs (i.e., MaOEA/C [32], MaOEA/IT [33] and PAEA [34]).…”

“…In order to further study the performance of the proposed PSO/CDLS, three competitive PSOs (i.e., MPSO/D [26], AGPSO [31], and MaPSO [15]) and three EAs with promising performance for solving MaOPs (i.e., MaOEA/C [32], MaOEA/IT [33], and PAEA [34]) were used for comparison.…”

“…AGPSO [31] and MaPSO [15]) and three promising MaOEAs (MaOEA/C [32], MaOEA/IT [33] and PAEA [34]) were also compared for performance verification. A brief introduction of each compared algorithm is given as follows.…”

“…5) AGPSO [31]: AGPSO presents an angular-distance-based PSO, in which an effective velocity update strategy is designed to enhance the search intensity around the selected leader particles.…”

Point Set Registration Based on Improved KL Divergence

“…However, in this type of algorithm, the accuracy of the transformation parameters between the point sets is affected by the point set modeling. In response to these shortcomings, some registration algorithms that do not need to solve the correspondence between points were proposed [12,[14][15][16][17].…”