Search · four archives
Search · four archives
28 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…
Asmaa M. Khalid, Hanaa M. Hamza, Seyedali Mirjalili, Khaid M. Hosny
A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the…
Majharulislam Babor, Olivier Paquet-Durand, Reinhard Kohlus, Bernd Hitzmann
'Bernd Hitzmann'] Makespan dominates the manufacturing expenses in bakery production. The high energy consumption of ovens also has a substantial impact, which bakers may overlook. Bakers leave ovens running until the final product is baked, allowing them to consume energy even when not in use. It results in energy…
Sowmya Ravichandran, Premkumar Manoharan, Deepak Kumar Sinha, Pradeep Jangir + 2 more
Focusing on practical engineering applications, this study introduces the Multi-Objective Resistance-Capacitance Optimization Algorithm (MORCOA), a new approach for multi-objective optimization problems. MORCOA uses the transient response behaviour of resistance-capacitance circuits to navigate complex optimization…
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…
Adam Chehouri, Rafic Younès, Adrian Ilinca, Jean Perron
Within the last 20 years, wind turbines have reached matured and the growing worldwide wind energy market will allow further improvements. In the recent decades, the numbers of research papers that have applied optimization techniques in the attempt to obtain an optimal design have increased. The main target of…
Deniz Akdemir, Julio Isidro Sánchez
Multi-objective optimization is an emerging field in mathematical optimization which involves optimization a set of objective functions simultaneously. The purpose of most plant and animal breeding programs is to make decisions that will lead to sustainable genetic gains in more than one traits while controlling the…
Ricardo Andrade, Mahdi Doostmohammadi, João L. Santos, Marie-France Sagot + 2 more
In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize…
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…
Jianhua Jiang, Jiaqi Wu, Jinmeng Luo, Xi Yang + 2 more
'Heming Jia'] The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to…
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…
Wusi Yang, Li Chen, Yi Wang, Maosheng Zhang
The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel…
Guo Yu, Lianbo Ma, Wei Du, Wenli Du + 1 more
—Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such…
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…
Sergio Garcia, Cong Trinh
A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules.…
Xi Lin, Yilu Liu, Xiaoyuan Zhang, Fei Liu + 2 more
Optimization Authors: ['Xi Lin' 'Yilu Liu' 'Xiaoyuan Zhang' 'Fei Liu' 'Zhenkun Wang' 'Qingfu Zhang'] Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods often focus on finding a set of Pareto…
Xin‐She Yang
Design problems in industrial engineering often involve a large number of design variables with multiple objectives, under complex nonlinear constraints. The algorithms for multiobjective problems can be significantly different from the methods for single objective optimization. To find the Pareto front and…
Nazmul Hasan
The aim of this literature is to illustrate the application of multi-objective optimization routines through a case study of face milling operation. For this purpose, the face milling operation is designed as a multi-objective optimization problem and then solved to obtain optimum values for the machining parameters–…
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…
Robert Z. Shrote, Addie M. Thompson
Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies and assist in decision making. In addition to simulations, visualization of a Pareto frontier among…
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…
Alina Selega, Kieran R. Campbell
Many practical applications require optimization of multiple, computationally expensive, and possibly competing objectives that are well-suited for multi-objective Bayesian optimization (MOBO) procedures. However, for many types of biomedical data, measures of data analysis workflow success are often heuristic and…
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…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
Marc Griesemer, Ali Navid
Multi-objective Optimization (MO) is an important tool for quantitative examination of the trade-offs faced by biological organisms. Using genome-scale constraint-based models of metabolism (GSMs), Multi-Objective Flux Analysis (MOFA) allows MO analyses of trade-offs among key biological tasks. The leading software…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Owen Madin, Michael Shirts
Dispersion-repulsion interactions, commonly represented in atomistic force fields by the Lennard-Jones (LJ) potential, play an important role in the accuracy of molecular simulations. Training the force field parameters used in the LJ potential is challenging, generally requiring adjustment based on simulations of…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…