Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation

2007 ◽  
Vol 21 (21) ◽  
pp. 2897-2909 ◽  
Author(s):  
M. Janga Reddy ◽  
D. Nagesh Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Reservoir Operation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Trade Offs

scholarly journals Multi-objective reservoir operation of the Ukai reservoir system using improved Jaya algorithm

2021 ◽  
Author(s):  
Vijendra Kumar ◽  
S. M. Yadav
Keyword(s):  
Particle Swarm Optimization ◽  
Reservoir Operation ◽  
Particle Swarm ◽  
Jaya Algorithm ◽  
Hydropower Generation ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Better Than ◽  
Self Adaptive

Abstract This paper introduces an effective and reliable approach based on multi population approach, namely self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results are compared with ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and Invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit was obtained by SAMP-JA and JA as 305092.99 . SAMP-JA was better than JA, PSO and IWO in terms of convergence. In Model-2, the maximum hydropower generation was achieved by SAMP-JA, JA and PSO as 1723.50 . While comparing the average hydropower generation SAMP-JA and PSO performed better than JA and IWO. In terms of convergence, SAMP-JA was better than PSO. In Model-3, self-adaptive multi-population multi objective Jaya algorithm (SAMP-MOJA) was better than multi objective particle swarm optimization (MOPSO) and multi objective Jaya algorithm (MOJA) in terms of maximum hydropower generation, and MOPSO was better than SAMP-MOJA and MOJA in terms of minimum irrigation deficiency. While comparing convergence, SAMP-MOJA was found to be better than MOPSO and MOJA. Overall, SAMP-JA was found to be outperforming than JA, POS and IWO.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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