ABSTRACT

In India, many major and medium reservoirs have been constructed to meet the fast-growing demands of irrigation, hydropower generation, drinking and industrial water supply. To achieve social and economic sustainability in water scarce areas, it is vital to efficiently manage available water resources. Indeed, water resources systems are complex and require systematic study to facilitate optimal planning and management decisions. The present study supports the strategies designed to check the deficit in water availability for Indira Sagar Reservoir system in Madhya Pradesh, India, using a novel approach of standard PSO combining two variants, elitism and variational parameters, namely a time-variant elitist mutation multi-objective particle swarm optimization (TV-EMMOPSO) . The performance of the proposed algorithm is compared with the optimal operation policies developed by multi-objective particle swarm optimization (MOPSO) and elitistmutation particle swarm optimization (EM-MOPSO). The results obtained can help the managers of Indira Sagar Reservoir plan better strategies in future.

摘要

在印度，已经建造了许多大型和中型水库，以满足灌溉，水力发电，饮用水和工业用水的快速增长的需求。为了在缺水地区实现社会和经济可持续性，有效管理可用水资源至关重要。确实，水资源系统很复杂，需要进行系统的研究以促进最佳的计划和管理决策。本研究支持一种旨在通过结合两种新的标准PSO方法（精英主义和变异参数，即时变精英主义变异多变量）来检查印度中央邦Indira Sagar水库系统缺水情况的策略。客观粒子群优化（TV-EMMOPSO）。将该算法的性能与多目标粒子群优化算法（MOPSO）和电子突变粒子群优化算法（EM-MOPSO）开发的最优操作策略进行了比较。获得的结果可以帮助Indira Sagar水库的管理者在将来计划更好的策略。