An Evolutionary Multitasking Optimization Framework for Constrained Multiobjective Optimization Problems

Qiao, Kangjia; Yu, Kunjie; Qu, Boyang; Song, Hui; Yue, Caitong

doi:10.1109/tevc.2022.3145582

Cited by 106 publications

(22 citation statements)

References 51 publications

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“…These two populations independently produced offspring and only exchanged information during the environmental selection. Qiao et al [33] suggested a evolutionary multitasking-based CMO framework (EMCMO). Compared with CCMO, EMCMO considers the types of problems and analyzes the information of different individuals.…”

Section: Literature Reviewmentioning

confidence: 99%

A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

Liang

Chen

Wang

et al. 2023

Complex Intell. Syst.

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Constrained multi-objective optimization problems (CMOPs) exist widely in the real world, which simultaneously contain multiple constraints to be satisfied and multiple conflicting objectives to be optimized. Therefore, the challage in addressing CMOPs is how to better balance constraints and objectives. To remedy this issue, this paper proposes a novel dual-population based constrained multi-objective evolutionary algorithm to solve CMOPs, in which two populations with different functions are employed. Specifically, the main population considers both objectives and constraints for solving the original CMOPs, while the auxiliary population is used only for optimization of objectives without considering constraints. In addition, a dynamic population size reducing mechanism is proposed, which is used to adjust the size of the auxiliary population, so as to reduce the consumption of computing resoruces in the later stage. Moreover, an independent external archive is set to store feasible solutions found by the auxiliary population, so as to provide high-quality feasible solutions for the main population. The experimental results on 55 benchmark functions show that the proposed algorithm exhibits superior or at least competitive performance compared to other state-of-the-art algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

Liang

Chen

Wang

et al. 2023

Complex Intell. Syst.

Self Cite

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“…In our experimental comparison, we used the inverted generational distance (IGD) [88] and the hypervolume (HV) [89] to evaluate the performance of different algorithms. The performance of the algorithm can be fully measured by the IGD and HV metrics [90,91].…”

Section: Performance Metricmentioning

confidence: 99%

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

Chen

et al. 2022

Appl Intell

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“…We calculate the probability of any source task i according to its historical success rate in helping the target task j iteration by iteration. We use q j = {q i j |i = 1, ..., n, i = j} obtained from (6) to denote the estimated helpfulness of each source task to the j th target task. With the obtained probabilities, the next step is to select n s source tasks from all n − 1 candidates.…”

Section: B Inter-task Knowledge Transfermentioning

confidence: 99%

“…Evolutionary MTO (EMTO) [11], [12] leverages evolutionary algorithms (EAs) [13] as the optimizer, aiming to unleash the potential of the implicit parallelism featured in EAs for solving MTO problems, where multiple optimization problems are addressed within a unified search space and knowledge is typically represented in the form of promising solutions and transferred via certain evolutionary operations such as crossover and mutation. The development of EMTO includes multifactorial evolutionary algorithm (MFEA) [11] that is one of the most representative EMTO built on the genetic algorithm (GA), multitasking coevolutionary particle swarm optimization (MT-CPSO) that employs multiple swarms for solving multiple tasks [14], an adaptive evolutionary multitask optimization (AEMTO) framework that can adaptively choose the source tasks with probabilities for each target task working with differential evolution (DE) [15], an evolutionary multitasking-based constrained multi-objective optimization (EMCMO) framework developed to solve constrained multiobjective optimization problems by incorporating GA [6], etc., from which different EAs are involved and their advantages are adopted to exchange knowledge among different tasks.…”

Section: Introductionmentioning

confidence: 99%

Multi-task Optimization Based Co-training for Electricity Consumption Prediction

Song¹,

Qin²,

Yan³

2022

Preprint

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Real-world electricity consumption prediction may involve different tasks, e.g., prediction for different time steps ahead or different geo-locations. These tasks are often solved independently without utilizing some common problem-solving knowledge that could be extracted and shared among these tasks to augment the performance of solving each task. In this work, we propose a multi-task optimization (MTO) based co-training (MTO-CT) framework, where the models for solving different tasks are co-trained via an MTO paradigm in which solving each task may benefit from the knowledge gained from when solving some other tasks to help its solving process. MTO-CT leverages long short-term memory (LSTM) based model as the predictor where the knowledge is represented via connection weights and biases. In MTO-CT, an inter-task knowledge transfer module is designed to transfer knowledge between different tasks, where the most helpful source tasks are selected by using the probability matching and stochastic universal selection, and evolutionary operations like mutation and crossover are performed for reusing the knowledge from selected source tasks in a target task. We use electricity consumption data from five states in Australia to design two sets of tasks at different scales: a) one-step ahead prediction for each state (five tasks) and b) 6-step, 12-step, 18step, and 24-step ahead prediction for each state (20 tasks). The performance of MTO-CT is evaluated on solving each of these two sets of tasks in comparison to solving each task in the set independently without knowledge sharing under the same settings, which demonstrates the superiority of MTO-CT in terms of prediction accuracy.

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An Evolutionary Multitasking Optimization Framework for Constrained Multiobjective Optimization Problems

Cited by 106 publications

References 51 publications

A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

Multi-task Optimization Based Co-training for Electricity Consumption Prediction

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