A metaheuristic-based framework for index tracking with practical constraints

Yuen, Man-Chung; Ng, Sin-Chun; Leung, Man-Fai; Che, Hangjun

doi:10.1007/s40747-021-00605-5

Cited by 23 publications

(10 citation statements)

References 63 publications

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“…In the first paper, "Robust programming for basin-level water allocation with uncertain water availability and policydriven scenario analysis" [12], the authors developed a robust water life cycle model to reduce the risks of inappropriate estimations of water availability within a river basin, and conducted a policy-driven scenario analysis to provide managerial implications in terms of ongoing water-saving policies. The results on case study of Min-Tuo River basin showed that equity is a necessity when considering the water allocation in a river basin, which enables a more sustainable mode of local water use, and that local citizens' willingness to follow the policies is key to relieve the water pressure.…”

Section: Other Data-driven Operations Management Problemsmentioning

confidence: 99%

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Data-driven operations managementWe can roughly divide the accepted 15 papers into four groups according to their topics: data-driven supply chain management [1-4], data-driven process scheduling [5-8], data-driven healthcare operations management [9][10][11], and other data-driven operations management problems [12][13][14][15]. In the following, we formally introduce related works in detail.

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Wang

Cheng

et al. 2022

Complex Intell. Syst.

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Section: Other Data-driven Operations Management Problemsmentioning

confidence: 99%

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confidence: 99%

Guest editorial on “data-driven operations management”

Wang

Cheng

et al. 2022

Complex Intell. Syst.

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“…Heuristic algorithms such as swarm intelligence algorithms are therefore preferred algorithms for solving optimization problems by providing near-optimal solutions in a feasible amount of time. For example, they are employed in the signalized traffic problem [1], in index tracking, [2] and for solving the traveling salesman problem [3], [4].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Continuous Dragonfly Algorithm Using Hill Climbing Local Search to Tackle the Low Exploitation Problem

2022

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Optimization problems are usually solved using heuristic algorithms such as swarm intelligence algorithms owing to their ability of providing near-optimal solutions in a feasible amount of time. An example of optimization problem is the training of artificial neural networks to obtain the most optimal connection weights. Artificial Neural Network (ANN), being the most prominent machine learning algorithm, has a multitude of applications in a myriad of areas. Recently, the use of ANNs has risen exponentially owing to its effective ability of making conclusions based on certain inputs. This ability is primarily achieved during the training phase of the ANN, which is a vital process prior to being able to use the ANN. Gradient descent-based algorithms, which are usually used for the training process, often encounter the problem of local optima, thus being unable to obtain the optimal connection weights of the ANN. Metaheuristic algorithms, including swarm intelligence algorithms, have been found to be a better alternative to train ANNs. The Dragonfly Algorithm (DA) is a swarm intelligence algorithm that has been found to be more effective than multiple swarm intelligence algorithms. However, despite having a good performance, it still suffers from a low exploitation. In this paper, we propose to further improve the performance of DA by using hill climbing as a local search so as to enhance its low exploitation. The optimized DA algorithm is then used for training artificial neural networks which are employed for classification problems. Based on the experimental results, the optimized DA algorithm has a higher effectiveness than the original DA as the ANNs trained by the optimized DA have a lower root mean squared error and a higher classification accuracy than the ones trained by the original DA.

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“…In research literature, various techniques have been proposed for balancing these two features of a metaheuristic algorithm [19][20][21]. In study [19], authors have proposed a new search strategy based on crossover and space expanding (SE) strategy -leader selection to improve optimizing features of the PSO.…”

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“…The authors named this approach a hybrid global leader selection (GLS) strategy. A framework based on joint heuristic approaches has been proposed for optimizing the index tracking problem [21]. In literature, these techniques or strategies are broadly classified into: 1) integrating a new search approach into basic heuristic algorithm, 2) heuristic algorithm's variants -adjusting the parameters, 3) neighborhood topologies based enhancement and multi-swarm strategies.…”

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confidence: 99%

An Orthogonal Learning Bird Swarm Algorithm for Optimal Power Flow Problems

Javaid

Niaz

Ahmed³

et al. 2023

IEEE Access

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A dominant statistical method, in which the best combination of factors' levels are predicted by analyzing a few representative combinations of factors' levels named as orthogonal experimental design (OED). The OED is an effective approach for analyzing the effect of multi-levels factors simultaneously and it works on orthogonal learning (OL) strategy. An evolutionary programming based heuristic method has two contradictory features such as exploration and exploitation, balancing in these features have significant impact on its optimization performance. We have applied an OED based auxiliary search strategy for enhancing performance of the bird swarm algorithm (BSA) by improving its exploitation search ability. It is a challenging task to keep balance among two contradictory features -exploration and exploitation of a heuristic approach, while addressing optimal power flow (OPF) problems in power systems. In this research study, we have proposed improved BSA (IBSA) for solving the OPF problems in thermal power systems. We have conducted a study of the OPF problems with objective functions -reducing electricity generation cost, emission pollution, and active power loss to measure the efficiency of proposed IBSA. In this work, we have utilized five benchmark functions and solved OPF problems using three IEEE test systems including IEEE-30 bus system, IEEE-57 bus system, and IEEE-118 bus system to verify stability, effectiveness, and performance of proposed IBSA. The statistical and simulation results have indicated that the proposed IBSA has better convergence, efficiency, and robustness features than the original BSA as well as other heuristic approaches. It is observed that lowest electricity generation cost 800.3975$/h on IEEE-30 bus system, 41663.5500$/h on IEEE-57 bus system, and 134941.0367$/h on IEEE-118 bus system have been achieved using proposed IBSA to address the OPF problems. Furthermore, in transmission lines of the power system network minimum active power loss 16.2869MW has been observed by conducting a case study on the IEEE 118-bus system based on the proposed IBSA approach.INDEX TERMS Orthogonal learning, bird swarm algorithm, Optimal power flow, smart power grid I. INTRODUCTION A. BACKGROUND AND MOTIVATION

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A metaheuristic-based framework for index tracking with practical constraints

Cited by 23 publications

References 63 publications

Guest editorial on “data-driven operations management”

Guest editorial on “data-driven operations management”

An Optimized Continuous Dragonfly Algorithm Using Hill Climbing Local Search to Tackle the Low Exploitation Problem

An Orthogonal Learning Bird Swarm Algorithm for Optimal Power Flow Problems

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