Coevolutionary multitasking for concurrent global optimization: With case studies in complex engineering design

Cheng, Ming; Gupta, Abhishek; Ong, Yew-Soon; Ni, Zhiwei

doi:10.1016/j.engappai.2017.05.008

Cited by 70 publications

(45 citation statements)

References 30 publications

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“…Specifically, several typical genetic strategies include SBX [6,17], ordered crossover [25], one-point crossover [26], guided differential evolutionary crossover [27], Gaussian mutation [6], swap mutation [25], polynomial mutation [17,28], and swap-change mutation [29]. The other three EAs, differential evolution (DE) [13,[30][31][32], particle swarm optimization (PSO) [31,[33][34][35], and genetic programming (GP) [23], are also utilized as fundamental algorithm for MTO paradigms.…”

Section: Multi-population Evolution Modelmentioning

confidence: 99%

“…Compared with single-population SBX crossover, two parents come from two different subpopulations (P k and P r ). For MT-CPSO (multi-tasking coevolutionary PSO), the across-population reproduction is provided as follows [33]:…”

Section: Multi-population Evolution Modelmentioning

confidence: 99%

“…In MT-CPSO, if a particle does not improve its personal best position over a prescribed consecutive generation, the acrosspopulation reproduction operator is then triggered [33]. Obviously, the greater the value of the prescribed iterations, the smaller the probability of across-population reproduction.…”

Section: Multi-population Evolution Modelmentioning

confidence: 99%

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

Wang¹,

Xu²,

Rong³

et al. 2019

IJCIS

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A B S T R A C TMulti-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for the selected task only. However, the theoretical and experimental evidence to this conclusion is not very convincing and especially, the coincidence relation between MFEA and multi-population evolution model is ambiguous and inaccurate. This paper aims to make an in-depth analysis of this relationship, and to provide more theoretical and experimental evidence to support the idea. In this paper, we clarify several key issues unsettled to date, and design a novel across-population crossover approach to avoid population drift. Then MFEA and its variation are reviewed carefully in view of multi-population evolution model, and the coincidence relation between them are concluded. MFEA is completely recoded along with this idea and tested on 25 multi-task optimization problems. Experimental results illustrate its rationality and superiority. Furthermore, we analyze the contribution of each population to algorithm performance, which can help us design more efficient multi-population algorithm for multi-task optimization. MTO paradigm [6]. If the optimization tasks happen to bear some commonality or complementarity, then the inclusion of knowledge transfer often leads to significant performance improvements relative to conventional EAs alone.

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Section: Multi-population Evolution Modelmentioning

confidence: 99%

Section: Multi-population Evolution Modelmentioning

confidence: 99%

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

Wang¹,

Xu²,

Rong³

et al. 2019

IJCIS

View full text Add to dashboard Cite

show abstract

“…Cheng et al [19] developed a particle swarm optimization based co-evolutionary multi-tasking approach for concurrent global optimization, and demonstrated its performance on synthetic functions and in real-world complex engineering design. Chen et al [20] proposed an evolutionary multi-tasking single-objective optimization approach based on the cooperative co-evolutionary memetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

Tan

2020

IEEE Trans. Fuzzy Syst.

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Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multitask optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multitask optimization, which integrates evolutionary computation and multi-task learning. This paper proposes a novel and easy-toimplement multi-tasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single-and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTGA outperformed all of them, and had lower computational cost than six of them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.

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“…Recently, MFEA has shown great potential on solving realworld problems like complicated engineering design [Cheng et al, 2017], capacitated vehicle routing Problem [Zhou et al, 2016] and so on. For instance, in engineering design, a variety of possible product designs are analysed and optimised during the conceptualisation phase (before one of them is finally chosen).…”

Section: Introductionmentioning

confidence: 99%

A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

Tang

Chen

Deng

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionary algorithm and provides an efficient way to evolve individuals for solving multiple tasks concurrently. Its efficiency is derived by implicitly transferring the genetic information among tasks. However, MFEA doesn't distinguish the information quality in the transfer compromising the algorithm performance. We propose a group-based M-FEA that groups tasks of similar types and selectively transfers the genetic information only within the groups. We also develop a new selection criterion and an additional mating selection mechanism in order to strengthen the effectiveness and efficiency of the improved MFEA. We conduct the experiments in both the cross-domain and intra-domain problems.

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Coevolutionary multitasking for concurrent global optimization: With case studies in complex engineering design

Cited by 70 publications

References 30 publications

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

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

Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

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