Swarm intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The maximin fitness function: multi-objective city and regional planning
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Using a distance metric to guide PSO algorithms for many-objective optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Preference-based multi-objective particle swarm optimization using desirabilities
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Self-organized invasive parallel optimization
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
Weighted preferences in evolutionary multi-objective optimization
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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This paper proposes a method to use reference points as preferences to guide a particle swarm algorithm to search towards preferred regions of the Pareto front. A decision maker can provide several reference points, specify the extent of the spread of solutions on the Pareto front as desired, or include any bias between the objectives as preferences within a single execution. We incorporate the reference point method into two multi-objective particle swarm algorithms, the non-dominated sorting PSO, and the maximinPSO. This paper first demonstrates the usefulness of the proposed reference point based particle swarm algorithms, then compare the two algorithms using a hyper-volume metric. Both particle swarm algorithms are able to converge to the preferred regions of the Pareto front using several feasible or infeasible reference points.