New Method for Multi-Objective Problem Based on Genetic Algorithm and Its Application in Reservoir Operation

You, Jinjun; Gao, Hong; Wang, Hao

doi:10.1142/9789812702838_0120

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…The process of this evolutionary operation adopts the new values based on Boltzmann rule: when an optimised value is adopted, a depraved value is also adopted with a certain probability. This rule is expressed as the following form: (Amor et al, 2006;Chen et al, 2006;You et al, 2003). Here, the SA algorithm is introduced into GA. A simple integration method of GA and SA is to generate new individuals with GA.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part I. Model development

Wang¹,

Sun²,

Song³

et al. 2009

Journal of Environmental Management

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Section: Simulated Annealing Algorithmmentioning

confidence: 99%

An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part I. Model development

Wang¹,

Sun²,

Song³

et al. 2009

Journal of Environmental Management

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“…At the beginning of this century, Chang Jianxia [33] coded an algorithm in decimal mode that is now widely used in reservoir operation. The genetic algorithm can also transform the multi-objective into a single-objective solution by constraint processing [34]. However, with the emergence of dimensionality, the genetic algorithm (GA) also derives the evolutionary algorithm for solving multi-objective models, which is the non-dominated sorting genetic algorithm (NSGA-II) [35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health

Bai

Liu

et al. 2020

Water

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Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. This paper first proposes the following three indicators: the ecological elasticity coefficient (f1), the power generation elasticity coefficient (f2), and the ecological power generation profit and loss ratio (k). This paper then conducts a multi-target single dispatching study on ecology and power generation in the lower Yellow River. A genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II) combining constraint processing and feasible space search techniques were used to solve the single-objective model with the largest power generation and the multi-objective optimal scheduling model considering both ecology and power generation. The calculation results show that: (1) the effectiveness of the NSGA-Ⅱcombined with constraint processing and feasible spatial search technology in reservoir dispatching is verified by an example; (2) compared with the operation model of maximizing power generation, the power generation of the target model was reduced by 0.87%, the ecological guarantee rate was increased by 18.75%, and the degree of the impact of ecological targets on the operating results was quantified; (3) in each typical year, the solution spatial distribution and dimensions of the single-target and multi-target models of change are represented by the Pareto-front curve, and a multi-objective operation plan is generated for decision makers to choose; (4) the f1, f2, and k indicators are selected to analyze the sensitivity of the five multi-objective plans and to quantify the interaction between ecological targets and power generation targets. Ultimately, this paper discusses the conversion relationship and finally recommends the best equilibrium solution in the multi-objective global equilibrium solution set. The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improving the ecological health of the lower Yellow River.

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“…In 2001, Bolat devised a linearization representation of the same objective and replaced heuristic algorithm with genetic algorithm [3]. In 2003, You et al [4] were devoted to the study of genetic algorithm, which promoted the multiobjective algorithm for the preassignment of airport gates. In 2005, Lim and Wang constructed a different model that is aimed at reducing the number of conflicts during aircraft operation, and the model was solved by combining tabu search with local search in heuristic algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

An Integrative Approach with Sequential Game to Real-Time Gate Assignment under CDM Mechanism

Liu

Zhang

Chen

et al. 2014

Mathematical Problems in Engineering

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This paper focuses on real-time airport gate assignment problem when small-scale or medium- to large-scale flight delays occur. Taking into account the collaborative decision making (CDM) of the airlines and the airport, as well as the interests of multiagent (airlines, airports, and passengers), especially those influenced by flight banks, slot assignment and gate assignment are integrated into mixed set programming (MSP), and a real-time gate assignment model is built and solved through MSP coupled with sequential game. By this approach, the delay costs of multiagent can be minimized simultaneously; the fuel consumption of each airline can be basically equalized; the computation time can be significantly saved by sequential game; most importantly, the collaboration of the airlines and the airport is achieved so that the transferring cost caused by the delay of flight banks can be decreased as much as possible. A case study on small-scale flight delays verifies that the proposed approach is economical, robust, timesaving, and collaborative. A comparison of the traditional staged method and the proposed approach under medium- to large-scale flight delays proves that the integrative method is much more economical and timesaving than the traditional staged method.

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New Method for Multi-Objective Problem Based on Genetic Algorithm and Its Application in Reservoir Operation

Cited by 5 publications

References 4 publications

An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part I. Model development

An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part I. Model development

Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health

An Integrative Approach with Sequential Game to Real-Time Gate Assignment under CDM Mechanism

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