316 citations · 1.3k across the 49 of their papers we have counts for
15 papers · 1 filter
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…
An Analysis of Control Parameters of MOEA/D Under Two Different Optimization Scenarios
Ryoji Tanabe, Hisao Ishibuchi
An unbounded external archive (UEA), which stores all nondominated solutions found during the search process, is frequently used to evaluate the performance of multi-objective evol…
Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE
Ryoji Tanabe, Hisao Ishibuchi
A decomposition-based multi-objective evolutionary algorithm with a differential evolution variation operator (MOEA/D-DE) shows high performance on challenging multi-objective prob…
A Niching Indicator-Based Multi-modal Many-objective Optimizer
Ryoji Tanabe, Hisao Ishibuchi
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective…
Non-elitist Evolutionary Multi-objective Optimizers Revisited
Ryoji Tanabe, Hisao Ishibuchi
Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs. This paper revisits the per…
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms
Ryoji Tanabe, Hisao Ishibuchi
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good…
A Review of Evolutionary Multi-modal Multi-objective Optimization
Ryoji Tanabe, Hisao Ishibuchi
Multi-modal multi-objective optimization aims to find all Pareto optimal solutions including overlapping solutions in the objective space. Multi-modal multi-objective optimization…
An Easy-to-use Real-world Multi-objective Optimization Problem Suite
Ryoji Tanabe, Hisao Ishibuchi
Although synthetic test problems are widely used for the performance assessment of evolutionary multi-objective optimization algorithms, they are likely to include unrealistic prop…
An Analysis of Quality Indicators Using Approximated Optimal Distributions in a Three-dimensional Objective Space
Ryoji Tanabe, Hisao Ishibuchi
Although quality indicators play a crucial role in benchmarking evolutionary multi-objective optimization algorithms, their properties are still unclear. One promising approach for…
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…
A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm
Yiming Peng, Hisao Ishibuchi
A multi-modal multi-objective optimization problem is a special kind of multi-objective optimization problem with multiple Pareto subsets. In this paper, we propose an efficient mu…
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…