Jul 2026· GECCO Companion· pp. 59-60· 0 citations· 2 references
Computer Science
TL;DR
This paper addresses the computation of finite-size approximations of the set of ϵ-locally optimal solutions of a multi-objective optimization problem (MOP), a problem relevant in multi-objective multimodal optimization (MMMO).
Abstract
Here we briefly summarize the main findings of the above-mentioned paper by Hernández et al., 2024 [3]. In this paper, we address the computation of finite-size approximations of the set of ϵ-locally optimal solutions of a multi-objective optimization problem (MOP), a problem relevant in multi-objective multimodal optimization (MMMO). We propose a bounded archiver, ArchiveUpdateLQ,ϵB, the algorithm LQ,ϵMOEA, which directly uses this archiver in selection, and a hybrid with a multi-objective continuation method for improved accuracy when gradient information is available. Numerical results demonstrate the benefits of the proposed methods.
Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective opti...
Regina C. L. C. de Sousa, Dênis E. C. Vargas, Elizabeth F. Wanner et al.· Journal of Heuristics· 0 citations
An objective-wise variable analysis method that first evaluates the sensitivity of each objective to all decision variables, and then comprehensively aggregates the sensitivity information across multiple objectives to estimate the overall importance of decision variables is proposed.
Chuanlong Ye, Fazhi He, Xiaoxin Gao et al.· Journal of King Saud Univers...· 0 citations
In this paper, we propose a proximal gradient method with adaptive linesearch for multiobjective optimization problems whose objective functions are weakly smooth, i.e., they have H\"older continuous gradients. The proposed method is parameter-free as we do not require prior knowledge of parameters related to the weak...
This paper presents a new algorithm addressing the problem of stochastic optimization where the cost function depends on a vector of uncertain parameters with known statistics. The algorithm is parameterized so as to address various stochastic formulations spanning from Expectation-focused to Value-at-Risk (VaR) as wel...
Multidisciplinary design optimisation (MDO) is a field of optimisation where problems are partitioned into a set of subproblems, or disciplines, with interactions between them. Multi-objective (MO)-MDO considers cases where multiple objectives exist at the problem or subproblem level. Of particular interest are MO-MDO...
Victoria Johnson, João A. Duro, V. Kadirkamanathan et al.· ACM Transactions on Evolutio...· 0 citations
A dynamic two-population co-evolutionary algorithm (CHEA), which balances feasibility, convergence and diversity at different stages by dynamically adjusting the number of offspring of the two populations by dynamically adjusting the number of offspring of the two populations.
Mengduo Yu, Sheng-Wei Wang, Keda Chen et al.· Cluster Computing· 0 citations
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