Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Using Disruptive Selection to Maintain Diversity in GeneticAlgorithms
Applied Intelligence
Maintaining Genetic Diversity in Genetic Algorithms through Co-evolution
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
A comparative study of probability collectives based multi-agent systems and genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Distributed optimization and flight control using collectives
Distributed optimization and flight control using collectives
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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
IEEE Transactions on Evolutionary Computation
Multiple objective optimisation applied to route planning
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within the Probability Collectives (PC) optimisation framework. PC is an alternative approach to optimization where the optimization process focusses on finding an ideal distribution over the solution space rather than an ideal solution. We describe one way in which MOO can be done in the PC framework, via using a Pareto-based ranking strategy as a single objective. We partially evaluate this via testing on a number of problems, and compare the results with state of the art alternatives. We find that this first multi-objective probability collectives (MOPC) approach performs competitively, indicating both clear promise, and clear room for improvement.