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An integrative approach to protein sequence design through multiobjective optimization

PLOS COMPUTATIONAL BIOLOGY 2024年第7期20卷 e1011953页

作者： Hong, Lu Kortemme, Tanja Univ Calif San Francisco Dept Bioengn & Therapeut Sci San Francisco CA 94143 USA Univ Calif San Francisco Quantitat Biosci Inst San Francisco CA 94143 USA Chan Zuckerberg Biohub San Francisco 94158 CA USA

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 evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications. Proteins are the fundamental building blocks of life, and engineering them has broad applications in medicine and biotechnology. Computational methods that seek to model and predict the protein sequence-structure-function relationship have seen significant advancement from the recent development in deep learn

关键词： Genetic algorithms Optimization Entropy Protein folding Principal component analysis evolutionary genetics evolutionary algorithms Protein structure

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Localized Constrained-Domination Principle for Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第3期54卷 1376-1387页

作者： Zhou, Jinlong Zhang, Yinggui Wang, Juan Suganthan, Ponnuthurai Nagaratnam Cent South Univ Sch Traff & Transport Engn Changsha 410075 Peoples R China Cent South Univ Forestry & Technol Sch Logist & Transportat Changsha 410004 Peoples R China Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar

The constrained-domination principle (CDP) is one of the most popular constraint-handling techniques (CHTs), since it is simple, nonparametric, and easily embedded in unconstrained multiobjective evolutionary algorithms (MOEAs). However, the CDP overly emphasizes the importance of feasibility, which may lead to the search getting stuck in some locally feasible regions or locally optimal, especially when encountering problems with discontinuous and/or narrow feasible regions. This article seeks to capitalize on the strengths of the CDP while overcoming its weaknesses. Accordingly, we propose a novel constrained MOEA (called MOEA/D-LCDP), in which the CDP is applied in a local manner. Unlike most CHTs that emphasize feasibility, which use the feasibility rule in the whole search space, the proposed localized CDP only adopts the CDP within the niche. That is, to maintain the diversity of the population, only solutions within the niche are compared based on the localized CDP. The niche radius is determined a priori by the acute angle between the current subproblem and its nearest subproblem. Additionally, a population-based status detection strategy is developed to allocate computing resources more rationally, and a diversity-enhanced CDP is designed to enhance the exploitation of the search. Comprehensive experiments conducted on four benchmark test suites with a total of 34 problems and three real-world applications demonstrate that MOEA/D-LCDP is very competitive with representative algorithms.

关键词： Constrained optimization constrained-domination principle (CDP) evolutionary algorithms local manner multiobjective optimization

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Automatic grouping of wind turbine types via multi-objective formulation for nonuniform wind farm layout optimization using an analytical wake model

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ENERGY CONVERSION AND MANAGEMENT 2024年 315卷

作者： Ribeiro, Anderson de Moura Hallak, Patricia Habib Lemonge, Afonso Celso de Castro Loureiro, Felipe dos Santos Univ Fed Juiz de Fora Grad Program Computat Modeling Juiz De Fora Brazil Univ Fed Sao Joao del Rei Dept Thermal & Fluid Sci Sao Joao Del Rei Brazil

This study introduces a novel methodology that aims to minimize the number of different wind turbine types used in nonuniform wind farms. This approach involves a trade-off to tackle the logistical challenges associated with installing, operating, and maintaining such wind farms. To accomplish this, a Gaussian analytical wake model is used in conjunction with evolutionary algorithms (NSGA-II and SMS-EMOA) for multi-objective optimization. A multi-objective problem is formulated, regarding wind farm capacity factor (CF) and levelized cost of energy (LCOE), and addressed using two distinct approaches. In the first approach, proposed here, the number of different wind turbine types is limited and treated as an additional objective. In contrast, the second approach allows for flexibility in choosing any number of different turbine types. Comparing the results obtained, it was observed that the methodology presented here yielded a reduced number of different turbine types without compromising CF and LCOE objectives. A multi-criteria decision maker was applied to extract the final non-dominated solutions from the obtained Pareto set. Finally, economic indicators show the profitability of these solutions.

关键词： Nonuniform wind farm layout optimization Analytical wake model Multi-objective optimization evolutionary algorithms

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A Task Offloading Algorithm using Multi-Objective Optimization under Hybrid Mode in Mobile Edge Computing

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MOBILE NETWORKS & APPLICATIONS 2024年第5期29卷 1577-1593页

作者： Hou, Haole Chai, Zhengyi Liu, Xu Li, Yalun Zeng, Yue Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Software Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China Jinling Inst Technol Sch Software Engn Nanjing 211199 Peoples R China

In mobile edge computing (MEC), edge clients have the capability to offload their computing tasks to edge servers or other edge clients, aiming to reduce their energy consumption and latency. However, the process of offloading introduces additional energy consumption and time due to long-distance network transmission, which presents a significant challenge in jointly minimizing energy consumption and latency for edge clients. To address this challenge, we formulate the problem as a constrained multi-objective optimization problem and propose an enhanced multi-objective evolutionary algorithm, named MOEA-TOMEC, to determine optimal task offloading strategies in MEC. The proposed algorithm aims to minimize both the extra energy consumption and latency. To overcome the limitations of a single offloading mode in complex network environments, we introduce a hybrid offloading mode within our algorithm. This hybrid approach combines partial offloading and full offloading to achieve lower energy consumption and latency. Additionally, we propose a device classification algorithm that plays a crucial role in initializing the population. This algorithm incorporates low-battery devices as relays in the offloading decisions, thereby enhancing population diversity. By generating a superior initial population and expanding the breadth of the learning space, our proposed algorithm achieves improved performance. Experimental results demonstrate the effectiveness of our proposed algorithm. In comparison to existing algorithms, our approach reduces the energy consumption and latency of edge clients by an average of 9.13% and 13.91%, respectively.

关键词： Mobile edge computing Multi-objective optimization evolutionary algorithms Energy consumption Delay

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MEAHO: Membrane evolutionary Algorithm for Hyperparameter Optimization of Deep Convolutional Neural Networks 38th

MEAHO: Membrane Evolutionary Algorithm for Hyperparameter Op...

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38th CCF National Conference of Computer Applications, CCF NCCA 2023

作者： Mu, Jie Hou, Jiaxin Wan, Ying School of Big Data and Software Engineering Chongqing University Chongqing400044 China

ISBN: (纸本)9789819987634

Membrane algorithm has been used to solve many optimization problems since it was put forward. These methods used the membrane algorithm as a container for other algorithms to solve many problems, such as, traveling salesman problem, the knapsack problem and so on. In this paper, from the angle of membrane algorithm, the solution of hyperparameter optimization problem by membrane algorithm is explored, with the hyperparameter space divided by lattice method, which transformed into membrane structure subsequently. For the evolution of membrane, the entropy reduction principle is proposed. Compared with other membrane algorithms, this paper solves the optimization problem from the membrane algorithm itself, designs the corresponding algorithm for the membrane structure, and obtains the experimental results through CNN experiment. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： evolutionary algorithms

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An Ensemble Surrogate-Based Framework for Expensive Multiobjective evolutionary Optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2022年第4期26卷 631-645页

作者： Lin, Qiuzhen Wu, Xunfeng Ma, Lijia Li, Jianqiang Gong, Maoguo Coello, Carlos A. Coello Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Xidian Univ Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China CINVESTAV IPN Comp Sci Dept Mexico City 07360 DF Mexico Basque Ctr Appl Math Bilbao 48009 Spain Ikerbasque Bilbao 48009 Spain

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will deteriorate, which inevitably worsens the performance of SAEAs. To deal with this issue, this article suggests an ensemble surrogate-based framework for tackling EMOPs. In this framework, a global surrogate model is trained under the entire search space to explore the global area, while a number of surrogate submodels are trained under different search subspaces to exploit the subarea, so as to enhance the prediction accuracy and reliability. Moreover, a new infill sampling criterion is designed based on a set of reference vectors to select promising samples for training the models. To validate the generality and effectiveness of our framework, three state-of-the-art evolutionary algorithms [nondominated sorting genetic algorithm III (NSGA-III), multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) and reference vector-guided evolutionary algorithm (RVEA)] are embedded, which significantly improve their performance for solving most of the test EMOPs adopted in this article. When compared to some competitive SAEAs for solving EMOPs with up to 30 decision variables, the experimental results also validate the advantages of our approach in most cases.

关键词： Optimization Computational modeling Predictive models evolutionary computation Uncertainty Training Space exploration Ensemble surrogate evolutionary algorithms model management multiobjective optimization

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Estimation of Distribution algorithms in Machine Learning: A Survey

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2024年第5期28卷 1301-1321页

作者： Larranaga, Pedro Bielza, Concha Univ Politecn Madrid Dept Inteligencia Artificial Madrid 28660 Spain

The automatic induction of machine learning models capable of addressing supervised learning, feature selection, clustering, and reinforcement learning problems requires sophisticated intelligent search procedures. These searches are usually performed in the possible model structure spaces, leading to combinatorial optimization problems, and in the parameter spaces, where it is necessary to solve continuous optimization problems. This article reviews how the estimation of distribution algorithms, a kind of evolutionary algorithm, can be used to address these problems. Topics include preprocessing, mining association rules, selecting variables, searching for the optimal supervised learning model (both probabilistic and nonprobabilistic models), finding the best hierarchical, partitional, or probabilistic clustering, obtaining the optimal policy in reinforcement learning, and performing inference and structural learning in Bayesian networks for association discovery. Interesting guidelines for future work in this area are also provided.

关键词： Bayesian networks combinatorial optimization continuous optimization estimation of distribution algorithms evolutionary algorithms machine learning Bayesian networks combinatorial optimization continuous optimization estimation of distribution algorithms evolutionary algorithms machine learning

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Revisiting 'survival of the fittest' principle in global stochastic optimisation: Incorporating anisotropic mutations

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2024年 130卷

作者： Kuzenkov, Oleg A. Morozov, Andrew Yu. Nalchajyan, Samvel A. Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Russian Acad Sci Inst Ecol & Evolut Moscow Russia Lobachevsky State Univ Nizhny Novgorod Nizhnii Novgorod Russia

evolutionary methods of global stochastic optimisation are very popular in the literature with various practical applications, including modelling of natural disasters. To enhance efficiency of optimisation, advanced algorithms seek to incorporate the heterogeneity of population (trial points) and consider more complex scenarios of mutations and recombination. On the other hand, most of the existing evolutionary algorithms have the major drawback that they cannot guarantee the convergence to the global extremum, in other words, such algorithms are purely heuristic. Here we proposed a new approach to global stochastic optimisation using evolutionary algorithms, where we can rigorously show convergence to the optimal solution with probability of one. This was a challenge for previous heuristic algorithms of optimisation. We provide the proof of convergence of the novel algorithm both for the case, where the parameter space has a finite dimensionality, and for optimisation in function spaces, with infinite number of dimensions. The crux of the proposed algorithms is to describe optimisation as a longterm natural selection process, which is a result of interplay between competition for limited resources and mutation, which is known as survival of the fittest principle. This is a modification of Monte-Carlo optimisation, where at each iteration, the search is refined by generating mutations to eventually attain the neighbourhood with the maximal population fitness. Unlike the previous algorithms, using survival of the fittest philosophy, we incorporate the anisotropic mutation for different spatial coordinates. We also suggest implementation of combination of the local and the global search. Using various multi-modal test functions in high dimensional spaces, we show that the new optimisation methods can demonstrate a better performance than some other well-known bio-inspired optimisation algorithms. As an important practical application, we implement our method to find

关键词： Global stochastic optimisation Convergence rate evolutionary algorithms Natural selection optimal control of infections

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A multi-objective optimization framework for functional arrangement in smart floating cities

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期237卷

作者： Kirimtat, Ayca Tasgetiren, M. Fatih Krejcar, Ondrej Buyukdagli, Ozge Maresova, Petra Univ Hradec Kralove Fac Informat & Management Ctr Basic & Appl Res Rokitanskeho 62 Hradec Kralove 50003 Czech Republic Baskent Univ Ind Engn Dept Ankara Turkiye Int Univ Sarajevo Dept Comp Sci Sarajevo Bosnia & Herceg

Before the terms "smart city" and "floating city" were introduced, the world's population had increased and land shortage across the world was already widely recognized. As a first challenge, the previous studies have developed the concept of a smart city as a creative answer, following that, several scientists proposed the floating city concept in the literature as a solution to the increased sea levels. Moreover, engineers, architects, and designers deal with city planning, for smart and floating settlements as a difficult design challenge, and evolutionary algorithms could be employed to address this complex problem by optimizing residents' needs. As a continuation of our previous studies on this topic, this time, we develop a multi-objective continuous genetic algorithm with differential evolution (DE) mutation strategy (MO_CGADE) and a multi-objective ensemble differential evolution algorithm (MO_EDE) to solve the problem on hand. Then, we compare the performance of the MO_CGADE and MO_EDE algorithms with the non-dominated sorting genetic algorithm (NSGAII) to maximize two conflicted objective functions, namely, scenery, and walkability in the proposed smart floating city model created in the Grasshopper Algorithmic Modeling Environment. The parametric model that we create in the Grasshopper software includes 64 decision variables, area constraints and objective functions to be optimized by MO_CGADE, MO_EDE, and NSGAII algorithms. Computational results show that MO_CGADE and MO_EDE algorithms generate better Pareto ranking results than the traditional NSGAII algorithm in terms of cardinality, distribution spacing, and coverage metrics.

关键词： evolutionary algorithms Floating city Smart city Multi-objective optimization

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Optimization of Fracturing Parameters with Machine-Learning and evolutionary Algorithm Methods

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ENERGIES 2022年第16期15卷 6063页

作者： Dong, Zhenzhen Wu, Lei Wang, Linjun Li, Weirong Wang, Zhengbo Liu, Zhaoxia Xian Shiyou Univ Dept Petr Engn Xian 710065 Peoples R China PetroChina Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China

Oil production from tight oil reservoirs has become economically feasible because of the combination of horizontal drilling and multistage hydraulic fracturing. Optimal fracture design plays a critical role in successful economical production from a tight oil reservoir. However, many complex parameters such as fracture spacing and fracture half-length make fracturing treatments costly and uncertain. To improve fracture design, it is essential to determine reasonable ranges for these parameters and to evaluate their effects on well performance and economic feasibility. In traditional analytical and numerical simulation methods, many simplifications and assumptions are introduced for artificial fracture characterization and gas percolation mechanisms, and their implementation process remains complicated and computationally inefficient. Most previous studies on big data-driven fracturing parameter optimization have been based on only a single output, such as expected ultimate recovery, and few studies have integrated machine learning with evolutionary algorithms to optimize fracturing parameters based on time-series production prediction and economic objectives. This study proposed a novel approach, combining a data-driven model with evolutionary optimization algorithms to optimize fracturing parameters. We established a significant number of static and dynamic data sets representing the geological and developmental characteristics of tight oil reservoirs from numerical simulation. Four production-prediction models based on machine-learning methods-support vector machine, gradient-boosted decision tree, random forest, and multilayer perception-were constructed as mapping functions between static properties and dynamic production. Then, to optimize the fracturing parameters, the best machine-learning-based production predictive model was coupled with four evolutionary algorithms-genetic algorithm, differential evolution algorithm, simulated annealing algorithm, and part

关键词： machine learning evolutionary algorithms production prediction net present value fracturing parameter optimization

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