Combining Aggregation with Pareto Optimization: A Case Study in Evolutionary Molecular Design

Kruisselbrink, Johannes W.; Emmerich, Michael; Bäck, Thomas; Bender, Andreas; Ijzerman, A. P.; Horst, Eelke van der

doi:10.1007/978-3-642-01020-0_36

Cited by 28 publications

(17 citation statements)

References 15 publications

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“…Input: P (current population), subCV s (set of groups of convergence-related variables) Output: P (next population) 1 F ront ← N ondominatedSort(P ); 2 Calculate the distance between each solution in P and the origin in objective space; 3 forall the Group ∈ subCV s do 4 nEvaluated ← 0; 5 while nEvaluated < |P | do 6 S ← ∅;…”

Section: Algorithm 3: Convergenceoptimization(p Subcv S)mentioning

confidence: 99%

A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

Zhang

Tian

Cheng

et al. 2018

IEEE Trans. Evol. Computat.

476

178

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Abstract-The current literature of evolutionary manyobjective optimization is merely focused on the scalability to the number of objectives, while little work has considered the scalability to the number of decision variables. Nevertheless, many real-world problems can involve both many objectives and large-scale decision variables. To tackle such large-scale many-objective optimization problems, this paper proposes a specially tailored evolutionary algorithm based on a decision variable clustering method. To begin with, the decision variable clustering method divides the decision variables into two types: convergence-related variables and diversity-related variables. Afterwards, to optimize the two types of decision variables, a convergence optimization strategy and a diversity optimization strategy are adopted. In addition, a fast non-dominated sorting approach is developed to further improve the computational efficiency of the proposed algorithm. To assess the performance of the proposed algorithm, empirical experiments have been conducted on a variety of large-scale many-objective optimization problems with up to 10 objectives and 5000 decision variables. Our experimental results demonstrate that the proposed algorithm has significant advantages over several state-of-the-art evolutionary algorithms in terms of the scalability to decision variables on many-objective optimization problems.

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Section: Algorithm 3: Convergenceoptimization(p Subcv S)mentioning

confidence: 99%

A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

Zhang

Tian

Cheng

et al. 2018

IEEE Trans. Evol. Computat.

476

178

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“…In many practical problems ranging from aircraft design [12] to molecular design [13] the number of objectives often exceeds three. Such optimization problems in the literature are referred to as many-objective optimization problems [6] .…”

Section: The Strategy For Implementingpareto Optimality For Many Objementioning

confidence: 99%

“…Evolutionary multiobjective optimization (EMO) algorithms that imitate the evolution process in nature to evolve a population of candidate solutions to generate a representative set of Pareto optimal solutions are commonly used for this purpose [11] . But the efficacy of most evolutionary multiobjective optimization algorithms, judged in terms of their ability to generate a diverse set of representative Pareto optimal solutions, in general, is limited to problems with up to two or three objectives [9] .In many practical problems ranging from aircraft design [12] to molecular design [13] the number of objectives often exceeds three. Such optimization problems in the literature are referred to as many-objective optimization problems [6] .…”

mentioning

confidence: 99%

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

Chugh

Chakraborti

Sindhya

et al. 2017

Materials and Manufacturing Processes

116

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A new data-driven reference vector guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using twelve process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.KEYWORDS: blast furnace, ironmaking, metamodeling, multi-objective optimization, model management, data-driven optimization, Pareto optimality INTRODUCTIONIron blast furnace is an immensely complex reactor and running it in an optimized fashion is a very complex task [1] . Although analytical models exist for this type of 2 reactors that produces hot metal [2] , such models are often quite cumbersome and of limited applicability in a real-life industrial scenario. In addition, a complete understanding of the blast furnace process involves handling several objectives together, which so far has been only marginally successful [3] . Thus, it is extremely complex, if not impossible, to build a simulator for blast furnace optimization and one has to rely upon limited amount of noisy data collected in daily operations to perform optimization.Another challenge in optimization of blast furnaces is that it involves multiple conflicting objectives, which is often known as multiobjective optimization [4] . The evolutionary algorithms have been widely used to solve multiobjective optimization problems [5] .However, the efficacy of most multiobjective evolutionary algorithms deteriorates as the number of objectives becomes more than four [4] , which makes them less suited for blast furnace optimization. Fortunately, many-objective optimization to solve problems with more than three objectives, has received increasing attention recently and many evolutionary algorithms have been developed for such problems [3,6] .Purely data-driven evolutionary optimization has received little attention with few exceptions. Most recently, Wang et al. [7] have also categorized data-driven optimization into two types: on-line and off-line. In on-line optimization, small amount of new data is available during the optimization while in off-line optimization, no extra data other than those in hands is available. The authors have also proposed a surrogate-based data-driven approach, capable of optimizing a trauma system involving two conflicting objectives in an evolutionary way. Although trauma system optimization belongs to offline data-driven 3 optimization [7] , there are a large amount of data available. By contrast, as indicated by Guo et al. [8] , off-line optimization becomes extremely challenging, when amount of historical data is small and noisy. Unfortunately, blast furnace optimization that is being...

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“…One approach to addressing this is to combine multiple individual parameters into a small number of 'scores' representing different factors that are then subject to Pareto optimization. For example, all of the parameters relating to ADME properties may be combined into a single ADME score and the trade-off explored with potency using Pareto optimization [29]. The integration of individual properties into a single score may be achieved using a method such as desirability functions or probabilistic scoring, as described below.…”

Section: B C Amentioning

confidence: 99%

Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties

Segall

2012

CPD

148

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A successful, efficacious and safe drug must have a balance of properties, including potency against its intended target, appropriate absorption, distribution, metabolism, and elimination (ADME) properties and an acceptable safety profile. Achieving this balance of, often conflicting, requirements is a major challenge in drug discovery. Approaches to simultaneously optimizing many factors in a design are broadly described under the term 'multi-parameter optimization' (MPO). In this review, we will describe how MPO can be applied to efficiently design and select high quality compounds and describe the range of methods that have been employed in drug discovery, including; simple 'rules of thumb' such as Lipinski's rule; desirability functions; Pareto optimization; and probabilistic approaches that take into consideration the uncertainty in all drug discovery data due to predictive error and experimental variability. We will explore how these methods have been applied to predicted and experimental data to reduce attrition and improve the productivity of the drug discovery process.

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Combining Aggregation with Pareto Optimization: A Case Study in Evolutionary Molecular Design

Cited by 28 publications

References 15 publications

A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties

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