Research Area: Metaheuristic Computing
In this work, two multi-objective versions of existing single-objective IMO were proposed. The first version is base don the Non-dominated Sorting Ions Motion Algorithm, i.e.,NSIMO. The NSIMO useselitist non-dominated sorting and crowding distance approaches to attain different non-domination levels and to retain diversity between the optimum set of solutions. The second version is achieved by integrating single-objective IMO with external storage and leader selection strategy.
This storage maintains the best non-dominated solutions obtained so far during optimization, and the leader selection strategy helps the search process towards the least crowded region of the Pareto front. The suggested methods we reapplied to several multi-objective benchmark functions having distinct characteristics. The outcomes were compared based on various performance metrics. The results obtained we reanalyzed with recently proposed algorithms. Both proposed approaches provided competitive, if not better, results for majority benchmark functions.
After confirming its efficacy on standard benchmark functions, the NSIMO and MOIMO were also applied for solving the multi-objective OPF problem on different test systems. The results were assessed in terms of different performance standards over 30 independent runs. The algorithms were also ranked for HV values based on the statistical tests.
Name of the Researcher: Hitarth Buch
Name of the Supervisor(s): Indrajit N Trivedi
Year of Completion: 2020
University: Gujarat Technological University
Thesis Link: Home Page Url
