Many-objective optimization with improved shuffled frog leaping algorithm for inter-basin water transfers

Guo, Yuxue; Tian, Xuemin; Fang, Guohua; Xu, Yue‐Ping

doi:10.1016/j.advwatres.2020.103531

Cited by 41 publications

(17 citation statements)

References 64 publications

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“…A decision maker must allocate limited water to different water use sectors considering the conflicting objectives (e.g., benefits and costs) and multiple uncertainties (e.g., forecast uncertainty) in a forecast-based reservoir operation system. Multi-objective programming (MOP) is a valuable tool for helping decision makers facilitate decision-making with multiple conflicting objectives (Fang et al, 2018b;Guo et al, 2020c), which can offer feasible methods for generating compromise decision alternatives. Some MOP approaches have been widely developed to tackle the uncertainty associated with the decision-making processes, such as multi-objective fuzzy programming (Zimmermann, 1978;Pishvaee and Razmi, 2012;Ren et al, 2017) and multiobjective stochastic programming (Xu et al, 2014(Xu et al, , 2020Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

Guo

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.

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Section: Introductionmentioning

confidence: 99%

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

Guo

et al. 2021

Hydrol. Earth Syst. Sci.

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“…Zhang et al proposed a hybrid multi-agent Coordination Optimization Algorithm (MCO) 15 that, applies a coordination mechanism to accelerate convergence. Guo et al presented a many-objective optimization with an improved shuffled frog leaping algorithm 16 . Liu et al proposed a novel multi-objective optimization algorithm based on Bacterial Foraging algorithm 17 .…”

Section: Introductionmentioning

confidence: 99%

An improved farmland fertility algorithm for many-objective optimization problems

Wang

Gao

Chen

2022

Sci Rep

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Recent studies on many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, difficulties in scalability upon MaOPs including the selection of leaders, etc., are encountered because the most evolutionary algorithms are proposed for single-objective optimization. To further improve the performance of many-objective evolutionary algorithms in solving MaOPs when the number of the objectives increases, this paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). In MOIFF, a novel bio-inspired meta heuristic method proposed in 2018, called Farmland Fertility algorithm (FF), is employed to serve as the optimization strategy. In order to handle MaOPs effectively, FF has been tailored from the following aspects. An individual fitness assessment approach based on cumulative ranking value has been proposed to distinguish the quality of each individual; a novel method based on individual cumulative ranking value to constitute and update the global memory and local memory of each individual is proposed, and a hybrid subspace search and full space search method has been designed to update individuals in the stages of soil optimization and soil fusion. In addition, adaptive environmental selection has been proposed. Finally, MOIFF is compared with four state-of-the art many-objective evolutionary algorithms on many test problems with various characteristics, including the DTLZ and WFG test suites. Experimental results demonstrate that the proposed algorithm has competitive convergence and diversity on MaOPs.

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“…Zhang HP et al (2019) proposed a hybrid multi-agent Coordination Optimization Algorithm (MCO) 11 , applying a coordination mechanism to accelerate convergence. Guo Y et al (2020) proposed a many-objective optimization with an improved shuffled frog leaping algorithm 12 . Liu Y et al (2020) proposed a novel multi-objective optimization algorithm based on bacterial foraging 13 .…”

Section: Introductionmentioning

confidence: 99%

An Improved Farmland Fertility Algorithm For Many-Objective Optimization Problems

Wang¹,

Gao²,

Chen³

2021

Preprint

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Recent studies in many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, they will encounter difficulties in their scalability upon MaOPs because the most evolutionary algorithms are proposed for single-objective optimization, which will affect the selection of leaders and individual-updating mechanisms, thereby deteriorating the effectiveness of the algorithm. This paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). A novel bio-inspired meta heuristic method proposed in 2018, called Farmland Fertility algorithm (FF), is employed to serve as the optimization strategy of MOIFF. In order to handle MaOPs effectively, FF has been tailored from the following aspects: an individual fitness assessment approach based on cumulative ranking value has been proposed to distinguish the quality of each individual, updated the global memory and local memory of each individual based on individual cumulative ranking value, and a hybrid subspace search and full space search method has been designed to update individuals in the stages of soil optimization and soil fusion. In addition, adaptive environmental selection has been proposed. Finally, MOIFF is compared with four state-of-the art many-objective evolutionary algorithms on many test problems with various characteristics, including the DTLZ and WFG test suites. Experimental results demonstrate that the proposed algorithm has competitive convergence and diversity on many-objective optimization problems.

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Many-objective optimization with improved shuffled frog leaping algorithm for inter-basin water transfers

Cited by 41 publications

References 64 publications

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

An improved farmland fertility algorithm for many-objective optimization problems

An Improved Farmland Fertility Algorithm For Many-Objective Optimization Problems

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