Solving Multi-objective Portfolio Optimization Problem for Saudi Arabia Stock Market Using Hybrid Clonal Selection and Particle Swarm Optimization

2015 ◽  
Vol 40 (8) ◽  
pp. 2407-2421 ◽  
Author(s):  
Sara A. Bin Shalan ◽  
Mourad Ykhlef
Keyword(s):  
Saudi Arabia ◽  
Particle Swarm Optimization ◽  
Stock Market ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Clonal Selection ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Portfolio Optimization Problem

Fuzzy Multi-objective Particle Swarm Optimization Solving the Three-Objective Portfolio Optimization Problem

2020 ◽  
Vol 22 (8) ◽  
pp. 2760-2768 ◽  
Author(s):  
Javier Alberto Rangel-González ◽  
Héctor Fraire ◽  
Juan Frausto Solís ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Portfolio Optimization Problem

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

2014 ◽  
Vol 971-973 ◽  
pp. 1242-1246
Author(s):  
Tie Jun Chen ◽  
Yan Ling Zheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

The mineral grinding process is a typical constrained multi-objective optimization problem for its two main goals are quality and quantity. This paper established a similarity criterion mathematical model and combined Multi-objective Dynamic Multi-Swarm Particle Swarm Optimization with modified feasibility rule to optimize the two goals. The simulation results showed that the results of high quality were achieved and the Pareto frontier was evenly distributed and the proposed approach is efficient to solve the multi-objective problem for the mineral grinding process.

scholarly journals Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

2021 ◽  
Vol 22 (9) ◽  
pp. 4408
Author(s):  
Cheng-Peng Zhou ◽  
Di Wang ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Protein Structure ◽  
Optimization Problem ◽  
Structure Refinement ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Energy Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Protein Structure Refinement

Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0.

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