Title :
Bi-objective portfolio optimization using Archive Multi-objective Simulated Annealing
Author :
Sen, Tanmay ; Saha, Sriparna ; Ekbal, Asif ; Laha, Arnab Kumar
Author_Institution :
Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Patna, Patna, India
Abstract :
In the current paper, Bi-objective portfolio optimization problem has been tackled using multiobjective optimization framework. Three popular multiobjective optimization algorithms are used for solving this problem. These are: Archive Multi-objective Simulated Annealing (AMOSA) algorithm, Non-dominated Sorting Genetic algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization using Crowding distance (MOPSOCD). For each algorithm we trace the Pareto optimal front and compare the results by using four comparisons metrics, Spread, Spacing, Set Coverage and Maximum Spread. Comparative results show that NSGA-II performs the best as compared to the other two algorithms.
Keywords :
Pareto optimisation; genetic algorithms; particle swarm optimisation; simulated annealing; MOPSOCD; NSGA-II; Pareto optimal front; archive multiobjective simulated annealing algorithm; biobjective portfolio optimization; multiobjective particle swarm optimization-using-crowding distance; nondominated sorting genetic algorithm II; Annealing; Frequency modulation; Measurement; Portfolios; Silicon; AMOSA; Comparison matrices; MOPSO-CD; NSGA-II; Portfolio optimization;
Conference_Titel :
High Performance Computing and Applications (ICHPCA), 2014 International Conference on
Print_ISBN :
978-1-4799-5957-0
DOI :
10.1109/ICHPCA.2014.7045343
