A modified particle swarm optimization algorithm with applications

He, Guang; Huang, Nan-jing

doi:10.1016/j.amc.2012.07.010

Cited by 28 publications

(14 citation statements)

References 12 publications

(14 reference statements)

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“…The solution portfolios obtained with the PSO solver outperformed those constructed using GA for all test problems in terms of Sharpe ratio and the established efficient frontier was above that of GA portfolios in all but one instance. Enhanced PSO algorithms for solving the multi-objective POP have been proposed by Deng et al [6] and He and Huang [12]. Cardinality and bounding constraints are incorporated by Deng et al [6], who find that their algorithm mostly outperforms GA, SA, and TS algorithms as well as previous PSO approaches especially in the case of low-risk portfolios.…”

Section: The Multi-objective Portfolio Optimization Problemmentioning

confidence: 99%

Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example

Doering

Juan

Ackah

et al. 2016

Lecture Notes in Business Information Processing

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Computational finance has become one of the emerging application fields of metaheuristic algorithms. In particular, these optimization methods are quickly becoming the solving approach alternative when dealing with realistic versions of financial problems, such as the popular portfolio optimization problem (POP). This paper reviews the scientific literature on the use of metaheuristics for solving rich versions of the POP and illustrates, with a numerical example, the capacity of these methods to provide high-quality solutions to complex POPs in short computing times, which might be a desirable property of solving methods that support real-time decision making.

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Section: The Multi-objective Portfolio Optimization Problemmentioning

confidence: 99%

Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example

Doering

Juan

Ackah

et al. 2016

Lecture Notes in Business Information Processing

View full text Add to dashboard Buy / Rent full text

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“…TSP is a NP-complete problem and it needs to be solved by some intelligent algorithms [19][20][21]. This paper utilized the discrete particle swarm algorithm [22] to search the optimal routes. Figure 6 illustrates the optimal fixed-routes for redistributing services in Tianyin Road metro station.…”

Section: Optimal Routes For Regular Redistributionmentioning

confidence: 99%

Determining the Optimal Layout Design for Public Bicycle System within the Attractive Scope of a Metro Station

Chen¹,

Chen

Jiang³

et al. 2015

Mathematical Problems in Engineering

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Public bicycle acts as a seamless feeder mode in combination with the citywide public transit, as well as a competitor for the inner-city short trips. The primary objective of this study is to address the layout planning of public bicycle system within the attracted scope of a metro station. Based on the land use function, population, and bicycle mode share, bicycle rental stations are divided into three types, namely, the metro station, district station, and resident station, and later the quantity of bicycle facilities in each rental station is estimated. Then, the service stations are selected from these bicycle rental stations to provide the service of periodical bicycle redistribution. An improved immune algorithm is proposed to determine the number and locations of service stations and the optimal route options for the implement of redistributing strategy. Finally, a case study of Nanjing Tianyin Road metro station is conducted to illustrate the proposed model and clarify some of its implementation details.

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“…Compared with other optimization algorithms, PSO has many advantages such as simple concept, high performance and fast convergence, and has attracted researchers' attention to solve various benchmark functions or real-world (Hsiao et al 2014;Pluhacek et al 2014) engineering problems (Coelho 2010;Jiang et al 2013;He and Huang 2012;Zhan et al 2013). However, the performance of PSO suffers from control parameters such as inertial weight and acceleration coefficients.…”

Section: Introductionmentioning

confidence: 98%

Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization

Liu

Ding

et al. 2014

Soft Comput

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Bare-bones particle swarm optimization (BPSO) is attractive since it is parameter free and easy to implement. However, it suffers from premature convergence because of quickly losing diversity, and the dimensionality of the solved problems has great impact on the solution accuracy. To overcome these drawbacks, this paper proposes an oppositionbased learning (OBL) modified strategy. First, to decrease the complexity of algorithm, OBL is not used for population initialization. Second, OBL is employed on the personal best positions (i.e., Pbest) to reconstruct Pbest, which is helpful to enhance convergence speed. Finally, we choose the global worst particle (Gworst) from Pbest, which simulates the human behavior and is called rebel learning item, and is integrated into the evolution equation of BPSO to help jump out local optima by changing the flying direction. The proposed modified BPSO is called BPSO-OBL, it has been evaluated on a set of well-known nonlinear benchmark functions in different dimensional search space, and compared with several variants of BPSO, PSOs and other evolutionary algorithms. Experimental results and statistic analysis confirm promising performance of BPSO-OBL on solution accuracy and convergence speed in solving majority nonlinear functions.

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A modified particle swarm optimization algorithm with applications

Cited by 28 publications

References 12 publications

Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example

Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example

Determining the Optimal Layout Design for Public Bicycle System within the Attractive Scope of a Metro Station

Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization

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