Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Kamboj, Vikram; Bath, S.; Dhillon, J.
• Is Part Of: Neural computing & applications. Volume 27:Issue 5(2016)
• Page Start: 1301
• Page End: 1316
• Publication Date: 2016-07
• Subjects: Biogeography-Based Optimization (BBO) -- Differential Evolution algorithm (DEA) -- Economic load dispatch problem (ELDP) -- Grey Wolf Optimizer (GWO) -- Unit commitment problem (UCP); 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/s00521-015-1934-8
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm inspired by grey wolves (Canis lupus), which simulate the social stratum and hunting mechanism of grey wolves in nature and based on three main steps of hunting: searching for prey, encircling prey and attacking prey. This paper presents the application of GWO algorithm for the solution of non-convex and dynamic economic load dispatch problem (ELDP) of electric power system. The performance of GWO is tested for ELDP of small-, medium- and large-scale power systems, and the results are verified by a comparative study with lambda iteration method, Particle Swarm Optimization algorithm, Genetic Algorithm, Biogeography-Based Optimization, Differential Evolution algorithm, pattern search algorithm, NN-EPSO, FEP, CEP, IFEP and MFEP. Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms.