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Found 8 papers, 0 papers with code
Applying Ising Machines to Multi-objective QUBOs
In this work, we extend the adaptive method based on averages in two ways: (i)~we extend the adaptive method of deriving scalarisation weights for problems with two or more objectives, and (ii)~we use an alternative measure of distance to improve performance.
What if we Increase the Number of Objectives? Theoretical and Empirical Implications for Many-objective Optimization
The presence of many objectives typically introduces a number of challenges that affect the choice/design of optimization algorithms.
Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics ---22 Years of Paradiseo---
The success of metaheuristic optimization methods has led to the development of a large variety of algorithm paradigms.
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms.
Surrogate Assisted Evolutionary Algorithm for Medium Scale Expensive Multi-Objective Optimisation Problems
The effectiveness of our proposed algorithm is validated on benchmark problems with 10, 20, 50 variables, comparing with three state-of-the-art SAEAs.
An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization
This work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set.
On the Impact of Multiobjective Scalarizing Functions
Recently, there has been a renewed interest in decomposition-based approaches for evolutionary multiobjective optimization.
Local Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with Correlated Objectives
Such local search algorithms typically return a set of mutually nondominated Pareto local optimal (PLO) solutions, that is, a PLO-set.
