A macro-evolutionary multi-objective immune algorithm with application to optimal allocation of water resources in Dongjiang River basins, South China

Liu, Dedi; Chen, Xiaohong; Shao, Quanxi; Xing-yuan, Song; Wang, Zhaoli

doi:10.1007/s00477-011-0505-5

Cited by 33 publications

(10 citation statements)

References 43 publications

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“…In this way, the sub-individuals whose membership is less than 0 a are gradually eliminated and are sub-individuals with large membership. So after a certain number of iterations, each individual is the solution that the decision maker wants [11][12] . Genetic algorithm steps is shown in Figure 1： …”

Section: The Establishment and Solution Of The Modelmentioning

confidence: 99%

Application of Interval Fuzzy Multi - objective Strategy Model in Water Resources Optimal Allocation

Zhu¹

2017

2017 International Conference on Computing, Communications and Automation (I3CA 2017)

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Abstract. Fuzzy multi-objective programming is an important optimization method to solve many complex practical problems. In this work, the applications of fuzzy multi-objective programming modeling for solving the practical problems in regional water resources optimal scheduling are studied. In order to strengthen the planning and management of water resources, the limited water resources are fully and effectively used scientifically. And because of the multi-objective and uncertainties of the regional water resources optimization scheduling problem, this paper adopts the fuzzy multi-objective programming method to deal with this complex practical problem. Based on the fuzzy multi-objective programming technique, the fuzzy multi-objective nonlinear programming model of regional water resources optimal dispatching is established. The multi-objective includes three goals: economic benefit, environmental benefit and social benefit. Then, the establishment and solving steps of the fuzzy multi-objective nonlinear programming model are introduced for the established model. Finally, the proposed scheme is evaluated and sorted. Finally, combined with the actual situation of a city to solve, verify the validity of the model.

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Section: The Establishment and Solution Of The Modelmentioning

confidence: 99%

Application of Interval Fuzzy Multi - objective Strategy Model in Water Resources Optimal Allocation

Zhu¹

2017

2017 International Conference on Computing, Communications and Automation (I3CA 2017)

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“…However, the traditional multiagent Q-learning methods have some shortcomings in water resource optimal allocation. For example, the computation of the methods based on the traditional multiagent Q-learning algorithms will be very complicated, because there are too many objectives to be optimized in water resource system [27], [28]. To deal with these problems, the individual objectives are abstracted into an agent-based model in this study and an improved multiagent Q-learning algorithm is proposed, which allows agents to self-learn.…”

Section: Introductionmentioning

confidence: 99%

A Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System

Liu

Ren

et al. 2014

IEEE Trans. Automat. Sci. Eng.

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Water environment system is a complex system, and an agent-based model presents an effective approach that has been implemented in water resource management research. Urban water resource optimal allocation is a challenging and critical issue in water environment systems, which belongs to the resource optimal allocation problem. In this paper, a novel approach based on multiagent Q-learning is proposed to deal with this problem. In the proposed approach, water users of different regions in the city are abstracted into the agent-based model. To realize the cooperation among these stakeholder agents, a maximum mapping value function-based Q-learning algorithm is proposed in this study, which allows the agents to self-learn. In the proposed algorithm, an adaptive reward value function is used to improve the performance of the multiagent Q-learning algorithm, where the influence of multiple factors on the optimal allocation can be fully considered. The proposed approach can deal with various situations in urban water resource allocation. The experimental results show that the proposed approach is capable of allocating water resource efficiently and the objectives of all the stakeholder agents can be successfully achieved.Note to Practitioners-Water resource optimal allocation is an important decision making activity in water resource management systems. This paper sets up a water resource optimal allocation model based on multiagent modeling technology, where different optimal objectives are abstracted into various properties of agents, and a new multiagent Q-learning approach is proposed to deal with the optimal allocation problem in water resource management system. The proposed approach can be used in practical water resource management systems, with any feasible data from the statistical agencies of government and companies. The experimental results demonstrate the effectiveness and efficiency of the agent-based allocation model and the proposed approach based on the novel multiagent Q-learning algorithm.Index Terms-Complex system, multiagent Q-learning, optimal allocation, urban water resource management.

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“…Water scarcity and water pollution increasingly plagued the world since the 1990s. The aims of the allocation of WPLE in integrated water-ecosystem-economy system have shifted from demand-decide-supply to the integrative allocation of water yield and water quality (Afzal et al 1992;Lind and Davalos-Lind 2002) and from pursuing maximum economic benefits to pursuing maximum integrative benefits of economy, society, and ecology (Liu et al , 2012Han et al 2011;Nouiri 2014). The research on WPLE increasingly concerns the harmonious and sustainable development of eco-environment and socioeconomy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Water Allocation Scheme in Integrated Water-Ecosystem-Economy System

Song

Liu

Arowolo

et al. 2017

River Basin Management

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The water crisis is one of the three crises that is persecuting the world. China is among the countries that face severe water shortages. Water scarcity and water pollution have seriously affected China's sustainable development in terms of the economy and society. Water resources per capita of China are only one quarter of world's average, and as much as 70% of China's rivers, lakes, and reservoirs are affected by pollution. Due to limited water resources, a crucial issue for the sustainable development of river basins relates to how to optimally allocate water resources and achieve a coordinated development of the economy, society, and ecology. On the basis of defining water consumption for production, living, and ecology, this chapter proposes a framework for forecasting and optimally allocating water consumption for production, living, and ecology (WPLE) in integrated water-ecosystem-economy system. Using Zhangye, in the middle reaches of the Heihe River Basin as the case study area, we forecasted and optimally allocated WPLE under three development scenarios, i.e., the conventional development scenario (CDS), the economy-priority development scenario (EPDS), and the environment sustainable development scenario (ESDS). In 2010, the proportions of WPLE in Zhangye were 87.73%, 2.74%, and 9.53%, respectively. In 2020, the proportions of WPLE will be 74.80%, 4.50%, and 20.70% under the CDS; 76.16%, 5.27%, and 18.57% under the EPDS; and 74.99%, 4.51%, and 20.50% under the ESDS. In the future, the proportion of production water consumption of Zhangye will drastically decrease, while the proportion of ecology water consumption will significantly increase. The main contradiction of the coevolution of WPLE of Zhangye is the competitiveness of production and living water consumption with ecology water consumption. Keywords Water allocation scheme Á Production water Á Living water Á Ecology water Á Water-ecosystem-economy system Á Scenario analysis Á Genetic algorithm Á Heihe River Basin Á Zhangye Water consumption Production water Living water Ecology water Agriculture Industry Industrial production water Agricultural irrigation water Rural areas Urban areas Urban population water Rural population water Artificial areas Natural areas Natural water and natural vegetation water Irrigation water and evaporation for artificial ecological grassland and woodland Rural livestocks water

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A macro-evolutionary multi-objective immune algorithm with application to optimal allocation of water resources in Dongjiang River basins, South China

Cited by 33 publications

References 43 publications

Application of Interval Fuzzy Multi - objective Strategy Model in Water Resources Optimal Allocation

Application of Interval Fuzzy Multi - objective Strategy Model in Water Resources Optimal Allocation

A Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System

Optimal Water Allocation Scheme in Integrated Water-Ecosystem-Economy System

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