Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
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…
Le-yang Gao, Rui Wang, Chuang Liu, Zhaohong Jia
—Multi-objective combinatorial optimization problems (MOCOPs), one type of complex optimization problems, widely exist in various real applications. Although meta-heuristics have been successfully applied to address MOCOPs, the calculation time is often much longer. Recently, a number of deep reinforcement learning…
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…
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…
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…
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…
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…
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…
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…
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…
Noor A. Rashed, Yossra H. Ali Tarik A. Rashid, Seyedali Mirjalili
- 1 Computer Sciences Dept., Univ. of Technology, Baghdad, Iraq; cs.20.62@grad.uotechnology.edu.iq - 2 Computer Sciences Dept., Univ. of Technology, Baghdad, Iraq; Yossra.h.ali@uotechnology.edu.iq - 3 Computer Sciences & Engineering Dept., Artificial Intelligence Centre and Innovation, Univ. of Kurdistan Hewler, Iraq…
Ruihao Zheng, Zhenkun Wang, Yin Wu, Maoguo Gong
—The ideal objective vector, which comprises the optimal values of the m objective functions in an m-objective optimization problem, is an important concept in evolutionary multi-objective optimization. Accurate estimation of this vector has consistently been a crucial task, as it is frequently used to guide the search…
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…
Shetty Nihal Naveen, B. Karthik Udupa, Ashil A. Shetty
Multi-objective optimization in drug discovery faces a fundamental limitation: the assumption that all relevant optimization criteria can be predefined. Traditional approaches using fixed objective sets often miss critical molecular trade-offs that emerge during the optimization process. We present AMODO-EO (Adaptive…
Tyler H. Chang, Stefan M. Wild
Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. This paper describes the design goals driving the…
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…
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…
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…
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…
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…