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dc.creatorChia-Hung Hsuen_US
dc.creatorChia-Feng Juangen_US
dc.description.abstractThis paper proposes a multi-objective, rule-coded,advanced, continuous-ant-colony optimization (MO-RACACO)algorithm for fuzzy controller (FC) design and its application tomulti-objective, wall-following control for a mobile robot. In theMO-RACACO-based FC design approach, the number of rulesand all free parameters in each rule are optimized using the MORACACOalgorithm. This is a complex multi-objective optimizationproblem that considers both the optimization of discrete variables(number of rules) and continuous variables (rule parameters).To address this problem, the MO-RACACO uses a rule-codedindividual (solution) representation and a rule-based mutationoperation to find Pareto-optimal solutions with different numbersof rules. New solutions in the MO-RACACO are generatedusing a pheromone-level-based adaptive elite-tournament pathselection strategy followed by a Gaussian sampling operation. TheMO-RACACO-based FC design approach is applied to a multiobjective,wall-following problem for a mobile robot. Three objectivesare defined so that the robot is collision-free, maintains aconstant distance from the wall, and moves smoothly at a highspeed. This automatic design approach avoids the time-consumingmanual design of fuzzy rules and the exhaustive collection ofinput-output training pairs. The performance of the MORACACO-based control is verified through comparisons withvarious multi-objective population-based optimization algorithms(MOPOAs) in multi-objective FC optimization problems. Thisstudy also includes experiments that demonstrate robot wallfollowingcontrol using an actual mobile robot.en_US
dc.relationComputational Intelligence Magazine, IEEE, Volume 8, Issue3.en_US
dc.titleMulti-objective Continuous-Ant-Colony-Optimized FC forRobot Wall-Following Controlen_US
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