Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Adaptive computational chemotaxis in bacterial foraging optimization: an analysis
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
The field of evolutionary multi-objective optimization (MOO) has witnessed an ever-growing number of studies to use artificial swarm behavior. In this paper authors have made an endeavor to minimize the computational burden, associated with global ranking methods and local selection modules used in many multi-objective particle swarm optimizers. Two different swarm strategies were employed for global and local search respectively using particle swarms and bacterial chemotaxis. In this paper the authors have shown comparative improvements of the proposed method namely MOBSO, with a benchmark evolutionary MOO method, NSGA-II. The paper also highlights the reduction in computational complexity for large populations, due to the proposed method.