Application of a Modified NSGA Method for Multi-Objective Static Distributed Generation Planning

Soroudi, Alireza; Ehsan, Mehdi

doi:10.1007/s13369-011-0077-1

Cited by 11 publications

(1 citation statement)

References 25 publications

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“…Genetic algorithm searches for the global optimal solution by iteration and update of the population, and iteration of the population is implemented through selection, hybridization and mutation operators. Non-dominated sorting genetic algorithm II (NSGA-II) is a non-dominated sorting-based multi-objective evolutionary algorithm (MOEA), which is able to find much better spread of solutions and better convergence near the true Pareto-optimal front for its non-dominated sorting mechanism [12,13,14]. In this paper, non-dominated sorting mechanism is used in GA, which is improved by the biological immune system theory ( referred to as IGA hereafter).…”

Section: Methodsmentioning

confidence: 99%

Multi-Objective Optimal Allocation for Regional Water and Land Resources

Fang

Yin

Huang

et al. 2014

AMM

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With rapid development of society and economy, the issue of water shortage has presently been more and more serious in China. Optimal water and land resources allocation, involving many aspects such as society, economy, ecology etc., is a rational approach to solve this problem. In this study, a substantially improved model, i.e., multi-objective optimal allocation, is established for coordinating the usage of water and land resources. The model was developed on the basis of Immune Genetic Algorithms (IGA), and it mainly includes three objectives and seven constraints. The results of case study show that there is no water shortage in the predicting year of 2020 in Dongtai City, Jiangsu Province by using the optimal allocation of water and land resources. The new optimal allocation proposed in this study has a positive influence to promote the economic and social harmonious development and the natural environment protection for coastal areas of China.

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Section: Methodsmentioning

confidence: 99%