Multi-Objective Artificial Bee Colony for designing multiple genes encoding the same protein

Gonzalez-Sanchez, Belen; Vega‐Rodríguez, Miguel A.; Santander-Jiménez, Sergio; Granado-Criado, José M.

doi:10.1016/j.asoc.2018.10.023

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…Previous research showed the relevance of applying metaheuristics in this context. In particular, MOABC is a highly promising proposal that attained significant results for this problem in previous work [5]. Moreover, MOABC is based on ABC (artificial bee colony) metaheuristic, which has been used in many different problems since its development, achieving very good results [9].…”

Section: Baseline Metaheuristic: Moabcmentioning

confidence: 99%

“…As discussed in [5], the strengths of MOABC's optimization engine allow this algorithm to achieve good results in the task of designing proteins encoded with multiple CDSs. However, the main weakness of this approach lies in the high execution times required to deal with large protein instances.…”

Section: Baseline Metaheuristic: Moabcmentioning

confidence: 99%

“…Regarding the configuration of parameters of the MOABC algorithm, we took as a reference the parametric studies performed in previous research [5]. Table 3 shows the parameter settings that allow MOABC to maximize its optimization capabilities in the tackled problem and to achieve a better mapping to the characteristics of the targeted hardware platform.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…After examining parallel performance, the next step in this study involves the assessment of the quality of solutions for AP-MOABC, in order to determine if the adoption of asynchronous strategies is able to preserve the search capabilities of the serial baseline MOABC [5]. For this purpose, we compare the results obtained by AP-MOABC with other methods from the literature, namely COOL [3] and Terai's Fig.…”

Section: Comparisons With Other Authors' Proposalsmentioning

confidence: 99%

“…However, the need to consider multiple optimization criteria simultaneously gave rise to the proposal of novel methods that apply multi-objective optimization strategies. Examples of multi-objective metaheuristics that have been applied in this context are the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [4], as in [22], and the multi-objective artificial bee colony algorithm (MOABC) devised in [5].…”

Section: Introductionmentioning

confidence: 99%

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Parallel multi-objective optimization approaches for protein encoding

2021

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One of the main challenges in synthetic biology lies in maximizing the expression levels of a protein by encoding it with multiple copies of the same gene. This task is often conducted under conflicting evaluation criteria, which motivates the formulation of protein encoding as a multi-objective optimization problem. Recent research reported significant results when adapting the artificial bee colony algorithm to address this problem. However, the length of proteins and the number of copies have a noticeable impact in the computational costs required to attain satisfying solutions. This work is aimed at proposing parallel bioinspired designs to tackle protein encoding in multiprocessor systems, considering different thread orchestration schemes to accelerate the optimization process while preserving the quality of results. Comparisons of solution quality with other approaches under three multi-objective quality metrics show that the proposed parallel method reaches significant quality in the encoded proteins. In addition, experimentation on six real-world proteins gives account of the benefits of applying asynchronous shared-memory schemes, attaining efficiencies of 92.11% in the most difficult stages of the algorithm and mean speedups of 33.28x on a 64-core server-grade system.

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Section: Baseline Metaheuristic: Moabcmentioning

confidence: 99%

Section: Baseline Metaheuristic: Moabcmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

Section: Comparisons With Other Authors' Proposalsmentioning

confidence: 99%