The key to solving multimodal multi-objective optimization problems is to achieve good diversity in the decision space. However, the existing algorithms usually adopt the reproduction operation based on random mechanism, which do not make full use of the distribution features of promising solutions in the population, resulting in the defects of the diversity of the obtained Parteo optimal solution sets. In order to solve the above problem, this paper proposes a multimodal multi-objective optimization evolutionary algorithm (MMOEA) based on generative adversarial networks (GANs). Specifically, we firstly design a classification strategy to distinguish good solutions from poor solutions. The solutions in the population are classified as real samples and fake samples by non-dominated selection sorting based on special crowding distance, and the training data of GANs are obtained. Secondly, a GANs-based offspring generation method is proposed. Through the adversarial training of GANs, the generator can simulate the distribution of promising solutions in the population and generate offspring with good diversity. Thirdly, an environment selection strategy based on GANs is constructed. By sorting the classification probability of the solutions output by the discriminator, the population are selected and updated. Finally, the proposed algorithm is compared with seven other competitive multimodal multi-objective optimization evolutionary algorithms on the CEC 2019 test suite and a real-word problem, and experimental results indicate its superior performance.

Traditional multimodal multi-objective evolutionary algorithms (MMOEAs) usually adopt the reproduction strategy based on meta-heuristic and fail to make full use of changes of multi-generation population distribution, which can help the population to further evolve and improve the exploitation ability of algorithms. To address this issue, this paper proposes a deep learning-based evolutionary algorithm (DLEA), which converts historical population information into time series and uses deep neural networks to predict the population distribution. Specifically, a transformer-based prediction model is constructed to reproduce promising offspring by capturing changes of population distribution in adjacent generations. Moreover, many MMOEAs focus only on global Pareto optimal solution sets (PSs) and ignore local PSs. Although local PSs are inferior to global PSs in terms of objective values, the cost of obtaining global PSs in some practical applications is huge. Local PSs with similar objectives to global PSs are acceptable alternatives for decision makers. Therefore, a difference-based attention mechanism is designed for archive update, which saves solutions on the global PSs and local PSs by calculating the attention value. Experimental studies indicate that DLEA outperforms other six competitive MMOEAs.

In multimodal multi-objective optimization problems (MMOPs), multiple solutions on different Pareto optimal solution sets (PSs) are mapped to the same point on the Pareto front. Considering these different solutions can provide users with richer decisions, the search of multiple PSs is crucial when solving MMOPs. To this end, many multimodal multi-objective evolutionary algorithms (MMOEAs) often employ intricate mechanisms to maintain the diversity of the offspring in mating selection, but ignore to learn PSs. In this paper, a data-driven evolutionary algorithm based on inductive graph neural networks (DEA-IGNN) is proposed to solve MMOPs, which successfully learns the PSs topology by the graph structure to generate offspring with good performance. Specifically, a graph topology construction method based on Euclidean distance in the decision space is designed. It determines the neighborhood by calculating the Euclidean distance of individuals in the decision space and establishes the topological relationships to construct the graphs representing of population distribution. On this basis, a model based on inductive graph neural networks is constructed to assist offspring reproduction, which can learn unknown nodes by sampling and aggregating existing information. Moreover, a data-driven reproduction strategy is proposed to predict offspring with the good diversity and convergence, which uses the traditional variation operators to generate training data and adopts these data to train the model. The proposed DEA-IGNN is implemented and compared with eleven competitive MMOEAs on three test suites and a practical problem. The experimental results show that DEA-IGNN has good performance.

Federated learning (FL) enables collaborative model training across massively distributed edge devices, such as Internet of Things (IoT) nodes. However, resource constraints impose a major challenge, as there exists a tradeoff between maximizing learning accuracy and minimizing communication overhead between the resource-limited devices. In this article, we present a device selection approach for heterogeneous FL systems based on multiobjective optimization (MOO) and knowledge transfer (KT). We formulate the resource constraint in federated optimization as a multiobjective problem, and obtain Pareto-optimal solutions balancing resource efficiency and test accuracy. Additionally, we introduce an innovative KT mechanism that propagates the globally optimal models obtained during MOO to subsequent FL tasks, further expediting convergence. The multiobjective formulation and KT provide new insights into efficient and robust FL for resource-constrained IoT applications. We conduct extensive experiments on real-world data sets. Results demonstrate that our method achieves up to 11% higher accuracy than state-of-the-art methods, while effectively mitigating resource constraints. Impact Statement—FL is an efficient algorithm that enables everything to be interconnected without sharing data. However, resource constraint is the main challenge for federated optimization problems. Although many works have proposed various solutions from different perspectives, these methods cannot simultaneously minimize the communication resource cost while ensuring algorithm performance. We propose an automatic device selection algorithm for federated systems based on MOO and KT. This work not only reduces the global resource usage rate of FL but also enables it to converge quickly.

The application of remote sensing (RS) for building change detection (BCD) is indispensable for assessing shifts in land use and surface dynamics. Nevertheless, off-the-shelf deep learning-based BCD methods often suffer from incomplete change boundaries and pseudo changes, due to the insufficient utilization of difference information from bitemporal remote sensing images. To address these issues, we propose a difference-aware multiscale feature aggregation network (DMFANet) aimed at investigating more representative forms of change representation in BCD tasks. To facilitate comprehensive bitemporal feature alignment and differencing, the feature modulation module (FMM) is designed, which focuses on developing semantically robust and contextually enriched pyramidal feature representations by channel-spatial modulation. Subsequently, the cross-domain difference enhancement module (CDEM) is introduced to accurately locate changed building areas with intricate details by capturing multi-perspective difference information through the construction of different domain dependencies. Moreover, we propose a multi-scale context aggregation module (MCAM) to effectively adapt to building scale variations by aggregating multiscale difference features under the guidance of contextual information while mitigating the interference of redundant difference information. The empirical outcomes firmly confirm the superiority of our stream-lined network over nine cutting-edge approaches on the LEVIR-CD, WHU-CD, and SYSU-CD benchmarks, excelling both in terms of accuracy and efficiency. Code and pretrained models are accessible at https://github.com/SallyRonionGit/DMFANet.