Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
D. Tarunika, Ashish Sharma
Multi-objective optimization problems (MOPs) demand algorithms that effectively balance convergence, diversity, and computational efficiency. To address this challenge, a novel Multi-Objective Human Evolutionary Optimization Algorithm (MOHEOA) is proposed, inspired by the dynamics of human societal evolution. MOHEOA…
Wenxing Wu, Liqin Tian, Junyi Wu, Lianhai Lin + 1 more
Many increasingly complex multi-objective optimization problems are emerging, and there is an urgent need to develop new multi-objective optimization algorithms to meet the challenges. This study introduces the Multi-Objective Dung Beetle Optimization Algorithm (MODBO), which integrates competitive and neighborhood…
Doaa El-Nagar, Ibrahim Zeidan, Mohamed Issa
The Multi-Objective Sinh-Cosh Optimization Algorithm (MOSCHO) is presented in this article based on the memorized technique. MOSCHO is an extension version of the recently proposed Sinh-Cosh optimizer for multiple objective optimizations. The memorized local optimum is integrated with the global optimal solution to…
Salomon, Ludovic, Dörfler, Daniel + 2 more
MOCVXPY is an open-source Python library for convex vector optimization. It is built on top of CVXPY, a domain-specific language for single-objective convex optimization. MOCVXPY enables practitioners to describe their convex vector optimization problem in an intuitive algebraic language, that closely follows the…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Mohammed Jameel, Hana Merah, Alaa M. Abd El-latif, Tareq M. Al-shami + 2 more
This paper presents a robust multi-objective optimization approach-the multi-objective starfish optimization algorithm (MOSFOA)-designed to address complex challenges in engineering design and optimal power flow analysis. As an advanced extension of the starfish optimization algorithm (SFOA), MOSFOA leverages…
Ngo, Lam, Ha, Huong + 4 more
Bayesian Optimization (BO) is a powerful tool for optimizing expensive blackbox objective functions. While extensive research has been conducted on the single-objective optimization problem, the multi-objective optimization problem remains challenging. In this paper, we propose MOBO-OSD, a multi-objective Bayesian…
Menghao Tang, Zimin Liang, Miqing Li
Scalability of evolutionary algorithms refers to assessing how their performance changes as problem size increases. In the area of multi-objective optimisation, research on the scalability of multi-objective evolutionary algorithms (MOEAs) has predominantly focussed on continuous problems. However, multi-objective…
Niclas Boehmer, Maximilian Wittmann
| 1. | Introduction Our Contributions 1.1. | 2 3 | | | | | | | | | | | |----|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------|--|--|--|--|--|--|--|--|--|--| |…
Xinnong Li, Mark Sale, James Craig, Keith Nieforth + 2 more
The selection of a “good” model usually involves a combination of objective and subjective criteria. Although many aspects of model quality can be expressed numerically, certain desirable characteristics remain difficult-or even impossible-to quantify precisely. Multi-objective optimization (MOO) provides a systematic…
Kecheng Su, Yaoyang Wang, Yikang Kong, Wenan Liu + 1 more
Multi-objective optimization problems have extensive application value in the fields of engineering and science, among which UAV path planning, as a typical application scenario, has attracted considerable attention. This study innovatively proposes a multi-objective extension of the Gold Rush Optimization algorithm…
Colin Lynch, Kaitlin Baudier, Douglas Montgomery, Meghan Barrett
Animal nutritionists seek to understand how animals regulate the intake and balance of multiple nutrients, yet the design and analysis of such experiments are often limited by how nutrient spaces are represented. The geometric framework for nutrition (GFN) provides a powerful means to visualize nutrient interactions…
Junhao Qiu, Xin Chen, Liang Ge, Liyong Lin + 2 more
Neighborhood search operators are critical to the performance of Multi-Objective Evolutionary Algorithms (MOEAs) and rely heavily on expert design. Although recent LLM-based Automated Heuristic Design (AHD) methods have made notable progress, they primarily optimize individual heuristics or components independently…
Edward Ma, James Morrissey, Shutong Duan, Ziqi Lu + 10 more
Process optimization for Chinese hamster ovary (CHO) cell culture remains a challenge in biopharmaceutical development because multiple interacting parameters jointly influence productivity and product quality attributes. Traditional design-of-experiments (DoE) methods, while systematic, become impractically expensive…
Augustina C. Amakor, Konstantin Sonntag, Sebastian Peitz
In multi-task learning, handling an increasing number of objectives can quickly become challenging, both in terms of the computational resources and the decision maker's capacity to choose appropriate trade-offs. A widely used approach is thus to aggregate the individual losses in a single loss function by a weighted…
Rachel Gaffney, Magnus Rattray, Jean-Marc Schwartz, Kate Meeson
There are huge variations in metabolic complexity between the different kingdoms of life. Whilst it has been shown that some simple, unicellular organisms such as E. coli direct their energetic resources towards maximising proliferation, the metabolic goals of more complex organisms are unclear. This is an especially…
Authors not listed
This article presents an overview about the state of the art in the development of structured packings for distillation applications. The focus is on highlighting different approaches including heuristic development cycles, the development of new packing structures, 3D-printing as tool for manufacturing, and…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
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Physics-based coarse-grained (CG) models are widely used in (bio)molecular simulations, yet their parameterization remains challenging and labor-intensive. In this work, we demonstrate how recently developed gradient-based optimization methods can substantially accelerate the refinement of CG force field (FF)…
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Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
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Discovery-oriented research is a fundamental pursuit in chemical and materials science, especially when objective-free or purpose-ambiguous exploration can yield unexpected novel compounds or materials. Recently, data-driven objective-free exploration methods have emerged to support such discovery in materials science.…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Jon Ahlinder, Patrik Waldmann
Optimum contribution selection (OCS) balances genetic gain and inbreeding by optimizing parental contributions to the next generation, but current implementations rely on point estimates of breeding values that discard the uncertainty inherent in genetic evaluations. We introduce CVaR-OCS, a novel formulation that…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…