Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

Wang, Na; Xu, Qingzheng; Rong, Fei; Yang, Jungang; Wang, Lei

doi:10.2991/ijcis.d.191004.001

Cited by 18 publications

(12 citation statements)

References 38 publications

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“…Solving the multi-task optimization problem in a natural way is the multipopulation evolution strategy, in which each subpopulation evolves and exploits separate search spaces independently in order to solve the corresponding task. As an example, in Figure 6, a multi-population evolution model is depicted to solve two tasks [51]. According to the multi-population evolution model of MTEC, various implementation approaches of each element proposed so far are described in detail in the following subsection.…”

Section: Basic Implementation Approaches Of Multi-task Evolutionary Computationmentioning

confidence: 99%

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Wang

et al. 2021

Mathematics

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Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, inter-population reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.

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Section: Basic Implementation Approaches Of Multi-task Evolutionary Computationmentioning

confidence: 99%

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Wang

et al. 2021

Mathematics

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“…It is necessary to mention that the termination condition of the classic MFEA is a given maximum of generations [6]. It is obviously unfair competition, as the number of candidate solutions evaluated at each iteration may be different [10]. In order to obtain authoritative and credible results, the total number of function evaluations is given as a termination condition of MFEA in this section.…”

Section: Influence Of the Probability Of Individual Learningmentioning

confidence: 99%

“…In multi‐population MFEA, two genetic operators (intra‐crossover and inter‐crossover) are explicitly transferred into an easy‐to‐understand form described by (1) and (2), and then executed [10]. One of the benefits is that the ratio of three reproduction processes is tuneable manually or automatically according to the application.…”

Section: Influence Of the Ratio Of Reproduction Processesmentioning

confidence: 99%

“…Accordingly, the basic structure of multi‐population MFEA is depicted in Fig. 2 [10]. Note that, (1) and (2) are different simulated binary crossover (SBX) approaches in one population P k and across two populations P k and P r ( k ≠ r ), respectively

x_{i *}^{k} = ({, \begin{matrix} 0.5 \times (][, ()(, 1 + γ) x_{i}^{k} + ()(, 1 - γ) x_{j}^{k}), rand < 0.5 \\ 0.5 \times (][, ()(, 1 + γ) x_{j}^{k} + ()(, 1 - γ) x_{i}^{k}), rand > 0.5 \end{matrix})

x_{i *}^{k} = ({, \begin{matrix} 0.5 \times (][, ()(, 1 + γ) x_{i}^{k} + ()(, 1 - γ) x_{j}^{r}), 0 < rand < 0.25 \\ 0.5 \times (][, ()(, 1 + γ) x_{j}^{k} + ()(, 1 - γ) x_{i}^{r}), 0.25 < rand < 0.5 \\ 0.5 \times (][, ()(, 1 + γ) x_{i}^{r} + ()(, 1 - γ) x_{j}^{k}), 0.5 < rand < 0.75 \\ 0.5 \times (][, ()(, 1 + γ) x_{j}^{r} + ()(, 1 - γ) x_{i}^{k}), 0.75 < rand < 1 \end{matrix})

…”

Section: Introductionmentioning

confidence: 99%

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Parameter analysis on multi‐factorial evolutionary algorithm

Zhang

Fei

et al. 2020

J. eng.

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“…Different from the classical MFEA, the multipopulation multitasking evolutionary algorithm no longer adopts one population but initializes K subpopulations according to the number of tasks. Individuals in a subpopulation evolve for a specific task throughout the optimization process [19].…”

Section: Mtea As Multipopulation Evolutionmentioning

confidence: 99%

Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables

Wang

Sun

et al. 2020

Complexity

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Recently, it was demonstrated that multitasking evolutionary algorithm (MTEA), a newly proposed algorithm, can solve multiple optimization problems simultaneously through a single run, breaking through the limitations of traditional evolutionary algorithms (EAs), with good convergence and exploration performance. As a novel algorithm, MTEA still has a lot of unexplored space. Generally speaking, the order of solution variables has no significant influence on the single-tasking EAs. To our knowledge, the effect of the order of variables in the multitasking scenario has not been explored. To fill in this research gap, three orders of variables in the multitasking scenario are proposed in this paper, including full reverse order, bisection reverse order, and trisection reverse order. An important feature of these orders of variables is that an individual can recover as himself after two times of changing the order of variables. In order to verify our idea, these orders of variables are embedded into MTEA. The experiment results revealed that the effect of the different orders of variables is universal but not significant enough in the practical application. Furthermore, tasks with high similarity and high degree of intersection are sensitive to the order of variables and get great impact between tasks.

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Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

Cited by 18 publications

References 38 publications

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Parameter analysis on multi‐factorial evolutionary algorithm

Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables

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