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

Abstract

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 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.

Stochastic Optimal Design of Household-Based Hybrid Energy Supply Systems Using Sample Average Approximation

“…Some of the standard multi-objective optimization algorithms that have been proposed and successfully been applied in various applications for decades [6,14,16,21,[66][67][68][69], are the nondominated sorting genetic algorithm II (NSGA-II), nondominated sorting genetic algorithm III (NSGA-III) [66], multi-objective genetic algorithm (MOGA), the multi-objective grey wolf optimizer (MOGWO) [67], etc. Each of these algorithms might be better than the other in at least one of the following criteria such as convergence, diversity preservation, and execution time [69,70]. erefore, it is necessary to conduct a detailed comparative analysis to decide the algorithm that solves the problem better in this paper.…”

Optimizing Furnace efficiency for Factory of Future using Cooperative Games

“…• Population size: 40 • Reference Points: (40, 90) and (10,278) • : 0.01 • Weights of objectives: (0.5, 0.5) Pareto-Optimal configurations were obtained as part of the optimization. Figure (2) shows the feasible solutions and the pareto-optimal frontier (shown in blue line). There are multiple configurations of manipulated variables that give the optimal values for the objectives.…”

“…It is difficult to obtain a single solution to the multi-objective optimization problem because improvement in one objective comes at the expense of another. These problems are usually solved using evolutionary algorithms such as NSGA-II, NSGA-III, EFRRR etc [2]. These techniques result in a set of optimal solutions which is usually called Pareto Optimal set.…”

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