Multi-Objective Evolutionary Algorithms’ Performance in a Support Role

2015 ◽  
Author(s):  
Matthew J. Woodruff ◽  
Timothy W. Simpson ◽  
Patrick M. Reed
Keyword(s):  
Evolutionary Algorithms ◽  
Conceptual Design ◽  
Parameter Space ◽  
Design Problem ◽  
Problem Formulation ◽  
Diagnostic Assessment ◽  
Multi Objective ◽  
Assessment Study

This paper presents a diagnostic assessment study, evaluating five leading multi-objective evolutionary algorithms (MOEAs) on their effectiveness, efficiency, reliability, and controllability on four different formulations of the same benchmark conceptual design problem, using the same underlying model. This assessment entails a broad sampling of the parameter space of each MOEA, for each problem formulation, requiring millions of optimization runs and trillions of model evaluations. The results of this assessment show the strengths and limitations of these MOEAs, establishing the Borg MOEA as a leading algorithm.

scholarly journals Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 361 ◽  
Author(s):  
Sergio Garcia ◽  
Cong T. Trinh
Keyword(s):  
Evolutionary Algorithms ◽  
Design Problem ◽  
Optimization Problems ◽  
Strain Engineering ◽  
Cell Design ◽  
Multi Objective Optimization ◽  
Large Space ◽  
Multi Objective ◽  
Strain Design ◽  
Application Problem

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. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has recently been proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. In our previous study, we mathematically formulated the modular cell design problem based on the multi-objective optimization framework. In this study, we evaluated a library of state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutions for modular cells compatible with many product synthesis modules. Furthermore, the best performing algorithm could provide better and more diverse design options that might help increase the likelihood of successful experimental implementation. We identified key parameter configurations to overcome the difficulty associated with multi-objective optimization problems with many competing design objectives. Interestingly, we found that MOEA performance with a real application problem, e.g., the modular strain design problem, does not always correlate with artificial benchmarks. Overall, MOEAs provide powerful tools to solve the modular cell design problem for novel biocatalysis.

scholarly journals A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

2016 ◽  
Vol 92 ◽  
pp. 172-185 ◽  
Author(s):  
Jazmin Zatarain Salazar ◽  
Patrick M. Reed ◽  
Jonathan D. Herman ◽  
Matteo Giuliani ◽  
Andrea Castelletti
Keyword(s):  
Surface Water ◽  
Evolutionary Algorithms ◽  
Water Reservoir ◽  
Diagnostic Assessment ◽  
Multi Objective

scholarly journals Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2018
Author(s):  
Mohammed Mahrach ◽  
Gara Miranda ◽  
Coromoto León ◽  
Eduardo Segredo
Keyword(s):  
Evolutionary Algorithms ◽  
Knapsack Problem ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Problem Formulation ◽  
Travelling Salesman ◽  
Multi Objective ◽  
Main Components ◽  
Pareto Fronts ◽  
Single Objective

One of the main components of most modern Multi-Objective Evolutionary Algorithms (MOEAs) is to maintain a proper diversity within a population in order to avoid the premature convergence problem. Due to this implicit feature that most MOEAs share, their application for Single-Objective Optimization (SO) might be helpful, and provides a promising field of research. Some common approaches to this topic are based on adding extra—and generally artificial—objectives to the problem formulation. However, when applying MOEAs to implicit Multi-Objective Optimization Problems (MOPs), it is not common to analyze how effective said approaches are in relation to optimizing each objective separately. In this paper, we present a comparative study between MOEAs and Single-Objective Evolutionary Algorithms (SOEAs) when optimizing every objective in a MOP, considering here the bi-objective case. For the study, we focus on two well-known and widely studied optimization problems: the Knapsack Problem (KNP) and the Travelling Salesman Problem (TSP). The experimental study considers three MOEAs and two SOEAs. Each SOEA is applied independently for each optimization objective, such that the optimized values obtained for each objective can be compared to the multi-objective solutions achieved by the MOEAs. MOEAs, however, allow optimizing two objectives at once, since the resulting Pareto fronts can be used to analyze the endpoints, i.e., the point optimizing objective 1 and the point optimizing objective 2. The experimental results show that, although MOEAs have to deal with several objectives simultaneously, they can compete with SOEAs, especially when dealing with strongly correlated or large instances.

scholarly journals Comparison of Multi-objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

2019 ◽  
Author(s):  
Sergio Garcia ◽  
Cong Trinh
Keyword(s):  
Evolutionary Algorithms ◽  
Design Problem ◽  
Optimization Problems ◽  
Strain Engineering ◽  
Cell Design ◽  
Multi Objective Optimization ◽  
Large Space ◽  
Multi Objective ◽  
Strain Design ◽  
Application Problem

AbstractA 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. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has been recently proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. The modular cell design problem was mathematically formulated using a multi-objective optimization framework.[1] In this study, we evaluated a library of the state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutions for modular cells compatible with many product synthesis modules. Furthermore, the best performing algorithm could provide better and more diverse design options that might help increase the likelihood of successful experimental implementation. We identified key parameter configurations to overcome the difficulty associated with multi-objective optimization problems with many competing design objectives. Interestingly, we found that MOEA performance with a real application problem, e.g., the modular strain design problem, does not always correlate with artificial benchmarks. Overall, MOEAs provide powerful tools to solve the modular cell design problem for novel biocatalysis.

ROM Based Problem Formulation in Conceptual Design: Algorithm and Case Study

2014 ◽  
Author(s):  
Ronaldo Gutierrez ◽  
Yong Zeng ◽  
Xuan Sun ◽  
Suo Tan ◽  
Xiaoguang Deng ◽  
...  
Keyword(s):  
Natural Language ◽  
Conceptual Design ◽  
Design Problem ◽  
Quality Management System ◽  
Problem Formulation ◽  
Total Quality ◽  
Design Problems ◽  
Design Algorithm ◽  
Problem Formulations

Problem formulations in natural language imply imprecision, ambiguity, incompleteness, conflict and inconsistency between requirements in a design problem. Recursive Object Model (ROM) based problem formulation in conceptual design extracts complete product requirement from design problems structured initially in natural language. Since ROM carries certain semantic and syntactic information implied in natural language, it is used to formulate a design problem through a question asking approach. The scope of this paper is to present an updated algorithm, question templates, rules and detailed procedures to ask generic questions based on ROM representations. Generic questions are needed for the clarification and extension of the meaning of a design problem in order to overcome the imprecisions, ambiguities, conflicts and inconsistencies of problem descriptions in natural language. The updated algorithm, question templates, rules and detailed procedures for asking generic questions are used in a case study to formulate the development of a Total Quality Management system (TQMS).

A Meta-Objective Approach for Many-Objective Evolutionary Optimization

2020 ◽  
Vol 28 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Dunwei Gong ◽  
Yiping Liu ◽  
Gary G. Yen
Keyword(s):  
Evolutionary Algorithms ◽  
Selection Criteria ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Problem Formulation ◽  
Multi Objective ◽  
Objective Space ◽  
Convergence Performance ◽  
Poor Convergence ◽  
The Many

Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inability to maintain both convergence and diversity in a high-dimensional objective space. Exiting approaches usually modify the selection criteria to overcome this issue. Different from them, we propose a novel meta-objective (MeO) approach that transforms the many-objective optimization problems in which the new optimization problems become easier to solve by the Pareto-based algorithms. MeO converts a given many-objective optimization problem into a new one, which has the same Pareto optimal solutions and the number of objectives with the original one. Each meta-objective in the new problem consists of two components which measure the convergence and diversity performances of a solution, respectively. Since MeO only converts the problem formulation, it can be readily incorporated within any multi-objective evolutionary algorithms, including those non-Pareto-based ones. Particularly, it can boost the Pareto-based algorithms' ability to solve many-objective optimization problems. Due to separately evaluating the convergence and diversity performances of a solution, the traditional density-based selection criteria, for example, crowding distance, will no longer mistake a solution with poor convergence performance for a solution with low density value. By penalizing a solution in term of its convergence performance in the meta-objective space, the Pareto dominance becomes much more effective for a many-objective optimization problem. Comparative study validates the competitive performance of the proposed meta-objective approach in solving many-objective optimization problems.

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

scholarly journals Search Acceleration of Evolutionary Multi-Objective Optimization Using an Estimated Convergence Point

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 129 ◽  
Author(s):  
Yan Pei ◽  
Jun Yu ◽  
Hideyuki Takagi
Keyword(s):  
Parameter Space ◽  
Statistical Tests ◽  
Distribution Characteristic ◽  
Multi Objective Optimization ◽  
Pareto Improvement ◽  
Pareto Solutions ◽  
Pareto Solution ◽  
Multi Objective ◽  
Objective Space ◽  
Convergence Point

We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms.

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