56 citations · 166 across the 19 of their papers we have counts for
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Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
Hisao Ishibuchi, Lie Meng Pang, Ke Shang
It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by a…
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. O…
Algorithm Configurations of MOEA/D with an Unbounded External Archive
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
In the evolutionary multi-objective optimization (EMO) community, it is usually assumed that the final population is presented to the decision maker as the result of the execution…
Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets
Weiyu Chen, Hisao Ishibuhci, Ke Shang
Subset selection is a popular topic in recent years and a number of subset selection methods have been proposed. Among those methods, hypervolume subset selection is widely used. G…
Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization
Hisao Ishibuchi, Lie Meng Pang, Ke Shang
In general, a multi-objective optimization problem does not have a single optimal solution but a set of Pareto optimal solutions, which forms the Pareto front in the objective spac…
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
Weiyu Chen, Hisao Ishibuchi, Ke Shang
Recently, the discretization of decision and objective spaces has been discussed in the literature. In some studies, it is shown that the decision space discretization improves the…