Numerical continuation methods: an introduction
Numerical continuation methods: an introduction
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Indicator based evolutionary algorithms have caught the interest of many researchers for the treatment of multi-objective optimization problems in the recent past since they deliver the desired approximation of the solution set and due to a usually better performance compared to dominance based algorithms. Nevertheless, these methods still suffer the drawback that many function evaluations are required to obtain a suitable representation of the solution set. The aim of this study is to present the Directed Search (DS) Method as local searcher within global indicator based optimization algorithms. For this, we will present the DS in the context of hypervolume maximization leading to both a new local search algorithm and a new memetic algorithm. Further, we will present first attempts to adapt the DS to a class of parameter dependent problems.