Distributed evolutionary optimization, in Manifold: Rosenbrock's function case study
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
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
Distributed optimization and flight control using collectives
Distributed optimization and flight control using collectives
A study of probability collectives multi-agent systems on optimization and robustness
Transactions on computational collective intelligence IV
A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
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We present a robustness study of the search of Probability Collectives Multi-agent Systems (PCMAS) for optimization problems. This framework for distributed optimization is deeply connected with both game theory and statistical physics. In contrast to traditional biologically-inspired algorithms, Probability-Collectives (PC) based methods do not update populations of solutions; instead, they update an explicitly parameterized probability distribution p over the space of solutions by a collective of agents. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. By comparing with genetic algorithms, we show that the PCMAS method appeared superior to the GA method in initial rate of decent, long term performance as well as the robustness of the search on complex optimization problems.