A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

Xu, Zhiwei; Zhang, Kai; Xu, Xin; He, Jifeng

doi:10.3389/fnbot.2019.00109

Cited by 16 publications

(8 citation statements)

References 53 publications

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“…The transfer spark was proposed to exchange information between different tasks in MTO-FWA [96]. The core idea is to bind a firework and its generated explosion sparks and guiding sparks into a task module to solve a specific problem.…”

Section: How To Knowledge Transfer Implicitlymentioning

confidence: 99%

“…Since the first establishment of MFEA, a number of MTEC algorithms have been proposed and successfully applied in many benchmark problems and real-world problems over the past few years, as summarized in Table 3. MFEA [11], MFEA [18], None [21], MFEA-GHS [22], G-MFEA [39], MFEA-II [41], ASCMFDE [42], PGEA [49], MFDE with AIM [50], MPEF-SHADE [82], MFMP [83], MFDE [85], MFPSO [85], SaM-MA [86], MT-CPSO [86], MDE-DVSM [87], MTMSO [90], CPSOM [92], AMFPSO [94], MTO-FWA [96], MFBSO [98], BSMTO [99], BSMTO-II [99], EMTSO-CCMA [101], MFEARR [104], DEMTO [105], MFEA-DV [110], EMT-RE [111], LDA-MFEA [113],…”

Section: Applications Of Multi-task Evolutionary Computationmentioning

confidence: 99%

“…Specifically, several typical genetic strategies include simulated binary crossover [18,79], ordered crossover (OX) [57,80], onepoint crossover [59,61], DE crossover [61], guided differential evolutionary crossover [81], partially mapped crossover (PMX) and two-point crossover (TPX) [71], Gaussian mutation [18], uniform mutation [61], swap mutation (SW) [57,80], polynomial mutation [53,79], DE mutation [61], mutation using the Powell search method [81], swap-change mutation [64], and one-point mutation [71]. The other EAs, differential evolution (DE) [82][83][84][85][86][87], particle swarm optimization (PSO) [85][86][87][88][89][90][91][92][93][94], artificial bee colony (ABC) [95], fireworks algorithm (FWA) [96], self-organized migrating algorithm (SOMA) [97], brain storm optimization (BSO) [98,99], Bat Algorithm (BA) [100], and genetic programming (GP) [61], are also utilized as fundamental algorithm for MTEC paradigms.…”

Section: Intro-population Reproductionmentioning

confidence: 99%

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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: How To Knowledge Transfer Implicitlymentioning

confidence: 99%

Section: Applications Of Multi-task Evolutionary Computationmentioning

confidence: 99%

Section: Intro-population Reproductionmentioning

confidence: 99%

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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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“…The same evolutionary metaheuristic, namely DE, is considered in [85] for modeling a similarity-guided evolutionary multitask optimization. Interesting is also the MM technique developed in [86], focused on the multitasking adaptation of the well-known Fireworks Algorithm [87].…”

Section: Implicit Knowledge Transfer Based Static Solversmentioning

confidence: 99%

Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

Osaba¹,

Martinez²,

Ser³

2021

Preprint

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In this work we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of Evolutionary Multitasking tackles multitask optimization scenarios by using as inspiration concepts drawn from Evolutionary Computation. The main purpose of this survey is to collect, organize and critically examine the abundant literature published so far in Evolutionary Multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can stimulate future efforts in this topic. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.

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“…Several approaches have since then embraced this concept over the years, encompassing hybrid solvers [64], modern metaphors [74], classical bio-inspired metaheuristics [70], or multipopulation schemes [39]. Furthermore, other alternatives to MFO have also been proposed in the form of new algorithmic schemes, such as coevolutionary multitasking [19], multitasking multi-swarm optimization [53], or the fireworks-based algorithms proposed in [65]. Despite this recent upsurge of new MFO and EM frameworks, MFEA still remains at the spearhead of the field [31].…”

Section: Evolutionary Multitasking and Multifactorial Optimizationmentioning

confidence: 99%

AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

Osaba¹,

Ser²,

Martinez³

et al. 2020

Preprint

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Transfer Optimization is an incipient research area dedicated to the simultaneous solving of multiple optimization tasks. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm for dealing with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms for exchanging knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps understand interactions between related optimization tasks. A comprehensive experimental setup is designed for assessing and comparing the performance of AT-MFCGA to that of other renowned evolutionary multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed by 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regards to the superior quality of solutions provided by AT-MFCGA with respect to the rest of methods, which are complemented by a quantitative examination of the genetic transferability among tasks along the search process.

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A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

Cited by 16 publications

References 53 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

Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

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