Particle swarm optimization with dynamic random population topology strategies for a generalized portfolio selection problem

2016 ◽  
Vol 16 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Qingjian Ni ◽  
Xushan Yin ◽  
Kangwei Tian ◽  
Yuqing Zhai
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Particle Swarm ◽  
Selection Problem ◽  
Swarm Optimization ◽  
Portfolio Selection Problem

scholarly journals A novel hybrid PSO-based metaheuristic for costly portfolio selection problems

2021 ◽  
Author(s):  
Marco Corazza ◽  
Giacomo di Tollo ◽  
Giovanni Fasano ◽  
Raffaele Pesenti
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Particle Swarm ◽  
Selection Problem ◽  
Computational Time ◽  
Swarm Optimization ◽  
Portfolio Selection Problem ◽  
Hybrid Metaheuristic ◽  
Unconstrained Model ◽  
Unconstrained Problem

AbstractIn this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization, which we tailor on a portfolio selection problem. To motivate and apply our hybrid metaheuristic, we reformulate the portfolio selection problem as an unconstrained problem, by means of penalty functions in the framework of the exact penalty methods. Our metaheuristic is hybrid as it adaptively updates the penalty parameters of the unconstrained model during the optimization process. In addition, it iteratively refines its solutions to reduce possible infeasibilities. We report also a numerical case study. Our hybrid metaheuristic appears to perform better than the corresponding Particle Swarm Optimization solver with constant penalty parameters. It performs similarly to two corresponding Particle Swarm Optimization solvers with penalty parameters respectively determined by a REVAC-based tuning procedure and an irace-based one, but on average it just needs less than 4% of the computational time requested by the latter procedures.

Materialized View Selection Using Swap Operator Based Particle Swarm Optimization

2021 ◽  
Vol 13 (1) ◽  
pp. 58-73
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Data Warehouse ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Selection Problem ◽  
Data Repository ◽  
Swarm Optimization ◽  
View Selection ◽  
Particle Swarm Optimization Technique ◽  
Swap Operator

The data warehouse is a key data repository of any business enterprise that stores enormous historical data meant for answering analytical queries. These queries need to be processed efficiently in order to make efficient and timely decisions. One way to achieve this is by materializing views over a data warehouse. An n-dimensional star schema can be mapped into an n-dimensional lattice from which Top-K views can be selected for materialization. Selection of such Top-K views is an NP-Hard problem. Several metaheuristic algorithms have been used to address this view selection problem. In this paper, a swap operator-based particle swarm optimization technique has been adapted to address such a view selection problem.

A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems

2011 ◽  
Vol 15 (4) ◽  
pp. 473-478
Author(s):  
Shamshul Bahar Yaakob ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Method ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Selection Problems ◽  
Hybrid Particle Swarm Optimization ◽  
Trade Offs

In modern portfolio theory, the basic topic is how to construct a diversified portfolio of financial securities to improve trade-offs between risk and return. The objective of this paper is to apply a heuristic algorithm using Particle Swarm Optimization (PSO) to the portfolio selection problem. PSO makes the search algorithm efficient by combining a local search method through self-experience with the global search method through neighboring experience. PSO attempts to balance the exploration-exploitation tradeoff that achieves efficiency and accuracy of optimization. In this paper, a newly obtained approach is proposed by making simple modifications to the standard PSO: the velocity is controlled and the mutation operator of Genetic Algorithms (GA) is added to solve a stagnation problem. Our adaptation and implementation of the PSO search strategy are applied to portfolio selection. Results of typical applications demonstrate that the Velocity Control Hybrid PSO (VC-HPSO) proposed in this study effectively finds optimum solution to portfolio selection problems. Results also show that our proposed method is a viable approach to portfolio selection.

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