Intelligent robotic systems: theory, design and applications
Intelligent robotic systems: theory, design and applications
Foundations of distributed artificial intelligence
Foundations of distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Distributed intelligence: extending the power of the unaided, individual human mind
Proceedings of the working conference on Advanced visual interfaces
Population distributions in biogeography-based optimization algorithms with elitism
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Biogeography-based optimization and the solution of the power flow problem
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
Markov Models for Biogeography-Based Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present hardware testing of an evolutionary algorithm known as biogeography-based optimization (BBO) and extend it to distributed learning. BBO is an evolutionary algorithm based on the theory of biogeography, which describes how nature geographically distributes organisms. We introduce a new BBO algorithm that does not use a centralized computer, and which we call distributed BBO. BBO and distributed BBO have been developed by mimicking nature to obtain an algorithm that optimizes solutions for different situations and problems. We use fourteen common benchmark functions to obtain results from BBO and distributed BBO, and we also use both algorithms to optimize robot control algorithms. We present not only simulation results, but also experimental results using BBO to optimize the control algorithms of mobile robots. The results show that centralized BBO generally gives better optimization results and would generally be a better choice than any of the newly proposed forms of distributed BBO. However, distributed BBO allows the user to find a less optimal solution to a problem while avoiding the need for centralized, coordinated control.