Adaptive Weighted Aggregation 2: More scalable AWA for multiobjective function optimization
IEEE Congress on Evolutionary Computation(2011)
摘要
Adaptive Weighted Aggregation (AWA) is a frame work of multi-starting optimization methods based on scalarization for solving multiobjective function optimization problems. It progressively generates new solutions to refine the approximation of the Pareto set or the Pareto front by the subdivision, and iteratively estimates the appropriate weight vector for scalarization in each search by the weight adaptation. Our recent study shows that AWA's solution set combinatorially increases for the number of objectives. In this paper, we propose a new subdivision and weight adaptation scheme of AWA to improve its scalability. Numerical experiments show the effectiveness of the proposed method.
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关键词
Pareto optimisation,approximation theory,combinatorial mathematics,iterative methods,set theory,AWA solution set,Pareto front,Pareto set approximation,adaptive weighted aggregation,iterative estimation,multiobjective function optimization,multistarting optimization method,weight adaptation scheme,weight vector
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