A new Non-Dominated Sorting Grey Wolf Optimizer (NS-GWO) algorithm: Development and application to solve engineering designs and economic constrained emission dispatch problem with integration of wind power. (June 2018)

• Record Type: Journal Article
• Authors: Jangir, Pradeep; Jangir, Narottam
• Is Part Of: Engineering applications of artificial intelligence. Volume 72(2018)
• Page Start: 449
• Page End: 467
• Publication Date: 2018-06
• Subjects: Non-Dominated -- Crowing distance -- NSGWO algorithm -- Multi-objective algorithm -- Economic Constrained Emission Dispatch; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.04.018
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This novel article presents the multi-objective version of the recently proposed the Grey Wolf Optimizer (GWO) known as Non-Dominated Sorting Grey Wolf Optimizer (NSGWO). This proposed NSGWO algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutions in achieve till the evolution of last iteration limit. The best solutions are then chosen from the collection of all Pareto optimal solutions using a crowding distance mechanism based on the coverage of solutions and leadership hierarchy of grey wolfs in nature to guide hunting of wolfs towards the dominated regions of multi-objective search spaces. For validate the efficiency and effectiveness of proposed NSGWO algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSGWO algorithm against Multi objective Colliding Bodies Optimizer (MOCBO), Multi objective Particle Swarm Optimizer (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and Multi objective Symbiotic Organism Search (MOSOS). The results of proposed NSGWO algorithm validates its efficiency in terms of Execution Time (ET) and effectiveness in terms of Generalized Distance (GD), Diversity Metric (DM) on standard unconstraint, constraint and engineering design problem in terms of high coverage and faster convergence.