A multiple criteria decision model for information system project selection
Computers and Operations Research
Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
A hybrid particle swarm optimization for job shop scheduling problem
Computers and Industrial Engineering
A fuzzy approach to R&D project portfolio selection
International Journal of Approximate Reasoning
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Individual predicted integral-controlled particle swarm optimisation
International Journal of Innovative Computing and Applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Multi-basin particle swarm intelligence method for optimal calibration of parametric Lévy models
Expert Systems with Applications: An International Journal
Fuzzy R&D portfolio selection of interdependent projects
Computers & Mathematics with Applications
A milp bi-objective model for static portfolio selection of R&D projects with synergies
Journal of Computer and Systems Sciences International
Hi-index | 12.06 |
Selecting the most appropriate projects out of a given set of investment proposals is recognized as a critical issue for which the decision maker takes several aspects into consideration. Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one. Consequently, we consider a multi-objective project selection problem in this study where total benefits are to be maximized while total risk and total coat must be minimized, simultaneously. Since solving an NP-hard problem becomes demanding as the number of projects grows, a multi-objective particle swarm with new selection regimes for global best and personal best for swarm members is designed to find the locally Pareto-optimal frontier and is compared with a salient multi-objective genetic algorithm, i.e. SPEAII, based on some comparison metrics with random instances.