From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Path Planning for Cooperating Robots Using a GA-Fuzzy Approach
Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Evolutionary multi-objective optimization in robot soccer system for education
IEEE Computational Intelligence Magazine
Recombinant rule selection in evolutionary algorithm for fuzzy path planner of robot soccer
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Evolutionary programming-based univector field navigation methodfor past mobile robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper proposes a multiobjective quantum-inspired evolutionary algorithm (MQEA) to design efficient fuzzy path planner of mobil robot. MQEA employs the probabilistic mechanism inspired by the concept and principles of quantum computing. As the probabilistic individuals are updated by referring to nondominated solutions in the archive, population converges to Pareto-optimal solution set. In order to evaluate the performance of proposed MQEA, robot soccer system is utilized as a mobile robot system. Three objectives such as elapsed time, heading direction and posture angle errors are designed to obtain robust fuzzy path planner in the robot soccer system. Simulation results show the effectiveness of the proposed MQEA from the viewpoint of the proximity to the Pareto-optimal set. Moreover, various trajectories by the obtained solutions from the proposed MQEA are shown to verify the performance and to see its applicability.