Self-Regulated Particle Swarm Multi-Task Optimization

Zheng, Xiaolong; Zhou, Deyun; Li, Na; Wu, Tao; Lei, Yu; Shi, Jiao

doi:10.3390/s21227499

Cited by 7 publications

(9 citation statements)

References 47 publications

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“…Therefore, an important issue is to estimate the similarity between tasks and encourage KT when the tasks are similar. To this aim, Zheng et al [32] proposed a self-regulated EMTO algorithm. In their proposed strategy, when the tasks show some similarity in the search process, the intensity of the cross-task KT will be enhanced.…”

Section: B Related Workmentioning

confidence: 99%

Orthogonal Transfer for Multitask Optimization

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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Knowledge transfer (KT) plays a key role in multitask optimization. However, most of the existing KT methods still face two challenges. First, the tasks may commonly have different dimensionalities, making the KT between heterogeneous search spaces very difficult. Second, the tasks may have different degrees of similarity in different dimensions, making that treating all dimensions with equal importance may be harmful to the KT process. To address these two challenges, this paper proposes a novel orthogonal transfer (OT) method that is enabled by a cross-task mapping (CTM) strategy, which can achieve high-quality KT among heterogeneous tasks. For the first challenge, the CTM strategy maps the global best individual of one task from its original search space to the search space of the target task via an optimization process, which can handle the difference in task dimensionality. For the second challenge, the OT method is performed on the CTM-obtained individual and a random individual of the target task to find the best combination of different dimensions in these two individuals rather than treating all the dimensions equally, so as to achieve high-quality KT. To verify the effectiveness of the proposed OT method and the resulted OT-based multitask optimization (OTMTO) algorithm, this paper not only uses the existing multitask optimization benchmark but also proposes a new benchmark test suite named multitask optimization problems with different dimensionalities. Comprehensive experimental results on the existing and the proposed benchmarks show that the proposed OT method and the OTMTO algorithm are very advantageous in providing high-quality KT and in handling the heterogeneity of search space in multitask optimization problems compared to the existing competitive evolutionary multitask optimization algorithms.

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Section: B Related Workmentioning

confidence: 99%

Orthogonal Transfer for Multitask Optimization

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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“…This new strategy is based on search direction instead of individuals, generating offspring as per the sum of an elite solutions of one task and a difference vector from another task. In [75], a solver coined as self-regulated evolutionary multitasking optimization is presented. This method introduces a self-regulated knowledge transfer scheme that establishes several innovative concepts such as ability vector or task-groups.…”

Section: From Mfea To Mfea-ii: Searching For Adaptabilitymentioning

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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“… 40 ( B ) Swarm particle approach: A n number of nodes are selected to 'host' a particle, where the vector that the particle will take is defined by the adjacent neighbors of the node where the particle is a given momentum t based on an initial random velocity. 41 Each node the particle travels through is a structure that is computed by molecular docking. ( C ) Swarm particle convergence: The swarm particle algorithm starts at time 0 with many random particles exploring the space through random vectors with their own magnitude, direction, and sense.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

Boswell¹,

Verga²,

Mackle³

et al. 2023

IDR

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The urgent need for SARS-CoV-2 controls has led to a reassessment of approaches to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. There are yet no clinically approved broad-spectrum antivirals available for beta-coronaviruses. Discovery pipelines for pan-virus medications against a broad range of betacoronaviruses are therefore a priority. A variety of marine natural product (MNP) small molecules have shown inhibitory activity against viral species. Access to large data caches of small molecule structural information is vital to finding new pharmaceuticals. Increasingly, molecular docking simulations are being used to narrow the space of possibilities and generate drug leads. Combining in-silico methods, augmented by metaheuristic optimization and machine learning (ML) allows the generation of hits from within a virtual MNP library to narrow screens for novel targets against coronaviruses. In this review article, we explore current insights and techniques that can be leveraged to generate broad-spectrum antivirals against betacoronaviruses using in-silico optimization and ML. ML approaches are capable of simultaneously evaluating different features for predicting inhibitory activity. Many also provide a semi-quantitative measure of feature relevance and can guide in selecting a subset of features relevant for inhibition of SARS-CoV-2.

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Self-Regulated Particle Swarm Multi-Task Optimization

Cited by 7 publications

References 47 publications

Orthogonal Transfer for Multitask Optimization

Orthogonal Transfer for Multitask Optimization

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

In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

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