Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Michael T. M. Emmerich, André H. Deutz
In almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Hao Chen, Weikun Li, Weicheng Cui
Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed…
Jiyizhe Zhang, Daria Semochkina, Naoto Sugisawa, David Woods + 1 more
Multi-objective Bayesian optimization (MOBO) has shown to be a promising tool for reaction development. However, noise is usually unavoidable during experiments and makes it challenging to find reliable solutions. In this study, we focus on finding a set of optimal reaction conditions using multi-objective Euclidian…
Ruochen Zhang, Bin Zhu, Zhaoxia Guo
Small-world effect plays an important role in the field of network science, and optimizing the small-world property has been a focus, which has many applications in computational social science. In the present study, we model the problem of optimizing small-world property as a multiobjective optimization, where the…
Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, Ahmed Salih
Algorithms, Applications, and Trends for Solving Complex Real-World Problems Authors: ['Noor A. Rashed' 'Yossra H. Ali' 'Tarik A. Rashid' 'Ahmed Salih'] Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years due to their potential for solving real-world problems in various…
Richard Allmendinger, Andrzej Jaszkiewicz, Arnaud Liefooghe, Christiane Tammer
'Christiane Tammer'] aThe University of Manchester, Alliance Manchester Business School, Booth Street West, Manchester M15 6PB, UK bPozna´n University of Technology, Institute of Computing Science, Piotrowo 2, 60-965 Pozna´n, Poland cUniversity of Lille, CNRS, Centrale Lille, Inria, UMR 9189 - CRIStAL, F-59000 Lille…
Tianyu Liu, Junjie Zhu, Lei Cao, Shu-Chuan Chu
For large-scale multiobjective evolutionary algorithms based on the grouping of decision variables, the challenge is to design a stable grouping strategy to balance convergence and population diversity. This paper proposes a large-scale multiobjective optimization algorithm with two alternative optimization methods…
Jiuyuan Huo, Liqun Liu
The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex multiobjective optimization problems. However, the multiobjective artificial bee colony (MOABC) algorithm has not been…
Amin Ibrahim, Azam Asilian Bidgoli, Shahryar Rahnamayan, Kalyanmoy Deb
—As the interest in multi- and many-objective optimization algorithms grows, the performance comparison of these algorithms becomes increasingly important. A large number of performance indicators for multi-objective optimization algorithms have been introduced, each of which evaluates these algorithms based on a…
Jaza M. Abdullah, Tarik A. Rashid, Bestan B. Maaroof, Seyedali Mirjalili
'Seyedali Mirjalili'] This paper proposes the multi-objective variant of the recently-introduced fitness dependent optimizer (FDO). The algorithm is called a Multi-objective Fitness Dependent Optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and…
Huan Guo, Yoshino Tatsuo, Lulu Fan, Ao Ding + 2 more
'Genyuan Xing'] In this paper, two novel algorithms are designed for solving biobjective optimization engineering problems. In order to obtain the optimal solutions of the biobjective optimization problems in a fast and accurate manner, the algorithms, which have combined Newton's method with Neumann series expansion…
Nicola Hallmann, Catalina Guerra-Cornejo, Karl Burgess, Charlotte Merzbacher + 1 more
The optimization of culture media is critical for improving the efficiency and cost of cellular production systems. Traditional approaches often rely on extensive experimental trials or statistical methods, which can be both costly and time-consuming. Here, we present gsMOBO, a computational approach for media design…
Vera Steinhoff, Pascal Kerschke, Christian Grimme
When dealing with continuous single-objective problems, multimodality poses one of the biggest difficulties for global optimization. Local optima are often preventing algorithms from making progress and thus pose a severe threat. In this paper we analyze how single-objective optimization can benefit from…
Paul Bello, Pedro Gallardo, Lorena Pradenas, Jacques A. Ferland + 1 more
Childhood obesity is an undeniable reality and has shown a rapid growth in many countries. Obesity at an early age not only increases the risks of chronic diseases but also produces a problem for the whole healthcare system. One way to alleviate this problem is to provide each patient with an appropriate menu that can…
Junfei Zhang, Yimiao Huang, Guowei Ma, Brett Nener
In engineering optimization problems, multiple objectives with a large number of variables under highly nonlinear constraints are usually required to be simultaneously optimized. Significant computing effort are required to find the Pareto front of a nonlinear multi-objective optimization problem. Swarm intelligence…
Otto I. Pulkkinen, Prson Gautam, Ville Mustonen, Tero Aittokallio
Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient…
Lu Hong, Tanja Kortemme
With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Tatsuya Yoshizawa, Shoichi Ishida, Tomohiro Sato, Masateru Ohta + 2 more
Molecular design using data-driven generative models has emerged as a promising technology, impacting various fields such as drug discovery and the development of functional materials. However, this approach is often susceptible to optimization failure due to reward hacking, where prediction models fail to accurately…
Babita K. Verma, Ahmad A. Mannan, Fuzhong Zhang, Diego A. Oyarzún
Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Ken Jenewein, Luca Torresi, Navid Haghmoradi, Attila Kormányos + 2 more
Experimental catalyst optimization is plagued by slow and laborious efforts. Finding innovative materials is key to advancing research areas for sustainable energy conversion, such as electrocatalysis. Artificial intelligence (AI)-guided optimization bears great potential to autonomously learn from data and plan new…