A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective inventory planning using MOPSO and TOPSIS
Expert Systems with Applications: An International Journal
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
| Hi-index | 12.05 |
This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use @e-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions.