A novel hybrid PSO–GWO approach for unit commitment problem. Issue 6 (August 2016)

• Record Type: Journal Article
• Authors: Kamboj, Vikram
• Is Part Of: Neural computing & applications. Volume 27:Issue 6(2016)
• Page Start: 1643
• Page End: 1655
• Publication Date: 2016-08
• Subjects: Grey Wolf Optimizer (GWO) -- Particle swarm optimization (PSO) -- Single-area unit commitment problem (SAUCP); Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1962-4
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Particle swarm optimization algorithm is a inhabitant-based stochastic search procedure, which provides a populace-based search practice for getting the best solution from the problem by taking particles and moving them around in the search space and efficient for global search. Grey Wolf Optimizer is a recently developed meta-heuristic search algorithm inspired by Canis-lupus. This research paper presents solution to single-area unit commitment problem for 14-bus system, 30-bus system and 10-generating unit model using swarm-intelligence-based particle swarm optimization algorithm and a hybrid PSO–GWO algorithm. The effectiveness of proposed algorithms is compared with classical PSO, PSOLR, HPSO, hybrid PSOSQP, MPSO, IBPSO, LCA–PSO and various other evolutionary algorithms, and it is found that performance of NPSO is faster than classical PSO. However, generation cost of hybrid PSO–GWO is better than classical and novel PSO, but convergence of hybrid PSO–GWO is much slower than NPSO due to sequential computation of PSO and GWO.