Current trends in evolutionary multi-objective optimization

In a short span of about 14 years, evolutionary multi-objective optimization (EMO) has established itself as a mature field of research and application with an extensive literature, many commercial softwares, numerous freely downloadable codes, a dedicated biannual conference running successfully four times so far since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. In this paper, we make a brief outline of EMO principles, some EMO algorithms, and focus on current research and application potential of EMO. Besides, simply finding a set of Pareto-optimal solutions, EMO research has now diversified in hybridizing its search with multi-criterion decision-making tools to arrive at a single preferred solution, in utilizing EMO principle in solving different kinds of single-objective optimization problems efficiently, and in various interesting application domains which were not possible to be solved adequately due to the lack of a suitable solution technique

Pareto Meets Huber: Efficiently Avoiding Poor Minima in Robust Estimation

International audienceRobust cost optimization is the task of fitting parameters to data points containing outliers. In particular, we focus on large-scale computer vision problems, such as bundle adjustment , where Non-Linear Least Square (NLLS) solvers are the current workhorse. In this context, NLLS-based state of the art algorithms have been designed either to quickly improve the target objective and find a local minimum close to the initial value of the parameters, or to have a strong ability to avoid poor local minima. In this paper, we propose a novel algorithm relying on multi-objective optimization which allows to match those two properties. We experimentally demonstrate that our algorithm has an ability to avoid poor local minima that is on par with the best performing algorithms with a faster decrease of the target objective

An adaptation reference-point-based multiobjective evolutionary algorithm

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics

Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design

This paper introduces a novel set of benchmark problems aimed at advancing research in both single and multi-objective optimization, with a specific focus on the design of human-powered aircraft. These benchmark problems are unique in that they incorporate real-world design considerations such as fluid dynamics and material mechanics, providing a more realistic simulation of engineering design optimization. We propose three difficulty levels and a wing segmentation parameter in these problems, allowing for scalable complexity to suit various research needs. The problems are designed to be computationally reasonable, ensuring short evaluation times, while still capturing the moderate multimodality of engineering design problems. Our extensive experiments using popular evolutionary algorithms for multi-objective problems demonstrate that the proposed benchmarks effectively replicate the diverse Pareto front shapes observed in real-world problems, including convex, linear, concave, and inverted triangular forms. The benchmark problems' source codes are publicly available for wider application in the optimization research community.Comment: 10 pages, 8 figure