Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and fiv...Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.展开更多
Most of works on the time complexity analysis of evolutionary algorithms havealways focused on some artificial binary problems.The time complexity of the algorithms forcombinatorial optimisation has not been well unde...Most of works on the time complexity analysis of evolutionary algorithms havealways focused on some artificial binary problems.The time complexity of the algorithms forcombinatorial optimisation has not been well understood.This paper considers the time complexity ofan evolutionary algorithm for a classical combinatorial optimisation problem,to find the maximumcardinality matching in a graph.It is shown that the evolutionary algorithm can produce a matchingwith nearly maximum cardinality in average polynomial time.展开更多
文摘Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.
基金supported by Engineering and Physical Sciences Research Council(GR/R52541/01)State Key Lab of Software Engineering at Wuhan University
文摘Most of works on the time complexity analysis of evolutionary algorithms havealways focused on some artificial binary problems.The time complexity of the algorithms forcombinatorial optimisation has not been well understood.This paper considers the time complexity ofan evolutionary algorithm for a classical combinatorial optimisation problem,to find the maximumcardinality matching in a graph.It is shown that the evolutionary algorithm can produce a matchingwith nearly maximum cardinality in average polynomial time.
