Crowdsourcing contests have become an important approach for organizations to tackle complex problems by gathering innovative solutions from globally distributed participants. However, as platforms always host an increasing number of concurrent contests, the voluntary and selective nature of participants usually leads to coordination difficulties and unpredictable outcomes, which present a critical challenge to guide multicontest settings toward success. To address this issue, this article proposes a novel and effective dynamic link prediction-based recommendation algorithm tailored for crowdsourcing platforms named the heterogeneous temporal graph generative adversarial network (HTGGAN). The HTGGAN leverages participants’ historical interaction data to predict the optimal set of participants for each future task. This ensures that participants’ skills and interests align with the contest requirements, thereby improving the success of crowdsourcing contests. Specifically, the HTGGAN contains four modules. First, the metapath-relation aggregation (MRA) module integrates information from multiple metapaths of the heterogeneous temporal graph (HTG) into a unified spatial embedding. Second, the group-relation aggregation (GRA) module incorporates learnable group-level features into node-level representation learning to enhance the expressiveness of the target node. Third, the across-time aggregation (ATA) module captures interactions between the target node and its temporal neighbors, enabling the learning of an initial spatiotemporal embedding. Finally, the optimized recommendation (OR) module incorporates a generative adversarial network and performs link prediction to recommend appropriate users for competitions or posts. Using a large real-world dataset constructed from the Kaggle platform, we demonstrate that HTGGAN outperforms several state-of-the-art algorithms across multiple metrics and delivers clear practical value.

As a challenging research topic in evolutionary multitask optimization (EMTO), evolutionary many-task optimization (EMaTO) aims at solving more than three tasks simultaneously. The design of the EMaTO algorithm generally needs to consider two major open issues, which are how to obtain useful knowledge from similar source tasks and how to effectively transfer knowledge to the target task. In this paper, we discover that knowledge structure plays a significant role in dealing with these two issues and propose a novel knowledge structure preserving-based evolutionary algorithm (KSP-EA) to efficiently solve many-task optimization problems. KSP-EA aims to achieve two goals, which are firstly to obtain useful structure-preserved knowledge from similar source tasks and secondly to effectively transfer both direct and indirect knowledge to the target task. To achieve the first goal, we propose a local-structure-preserved knowledge acquisition strategy that projects the knowledge of similar source tasks into a unified subspace without loss of the knowledge structure, thus enhancing the quality of the obtained knowledge. To achieve the second goal, we propose a tree-based knowledge propagation strategy that constructs a knowledge propagating tree to connect all the tasks and propagates knowledge along the edges of this tree. This way, the target task can obtain both direct and indirect knowledge, improving the effectiveness of knowledge transfer. We conduct extensive experiments on CEC19 and WCCI22 many-task optimization test suites and a real-world application scenario to evaluate the performance of KSP-EA. The experimental results show that our KSP-EA generally outperforms state-of-the-art algorithms.

Evolutionary computation (EC) is a kind of meta-heuristic algorithm that takes inspiration from natural evolution and swarm intelligence behaviors. In the EC algorithm, there is a huge amount of data generated during the evolutionary process. These data reflect the evolutionary behavior and therefore mining and utilizing these data can obtain promising knowledge for improving the effectiveness and efficiency of EC algorithms to better solve optimization problems. Considering this and inspired by the ability of human beings that acquire knowledge from the historical successful experiences of their predecessors, this paper proposes a novel EC paradigm, named knowledge learning EC (KLEC). The KLEC aims to learn from historical successful experiences to obtain a knowledge library and to guide the evolutionary behaviors of individuals based on the knowledge library. The KLEC includes two main processes named “learning from experiences to obtain knowledge” and “utilizing knowledge to guide evolution”. First, KLEC maintains a knowledge library model and updates this model by learning the successful experiences collected in every generation. Second, KLEC not only adopts the evolutionary operation but also utilizes the knowledge library model to guide individuals for better evolution. The KLEC is a generic and effective framework, and we propose two algorithm instances of KLEC, which are knowledge learning-based differential evolution and knowledge learning-based particle swarm optimization. Also, we combine the knowledge learning framework with several state-of-the-art EC algorithms, showing that the performance of the state-of-the-art algorithms can be significantly enhanced by incorporating the knowledge learning framework.

Knowledge transfer (KT) has been regarded as an efficient method in evolutionary multitask optimization (EMTO) by utilizing the information of other tasks to promote the optimization of the current task. Most KT methods achieve information communication across index-aligned dimensions. However, the index-aligned dimensions are not always similar or related, which is not always suitable for communication and causes the low efficiency in KT. Moreover, when the KT occurs in the heterogeneous tasks with different dimensions, the task with lower dimensions often pads the extra dimensions to make their dimensions equal. However, the dimension-padding often involves the redundant or useless information, which may mislead the KT process. In this article, a novel multilevel and multisegment learning multitask optimization (MMLMTO) algorithm based on niching technique is proposed to achieve high-quality KT. First, a multilevel learning strategy is proposed to divide the population into three levels according to fitness values for better selecting the individuals for KT. Second, a multisegment learning strategy is proposed to split some top individuals in each level into several segments, and each segment will find its closest segment to form a niche, where the KT is executed. This ensures that KT occurs in the similar or related dimensions and avoids the dimension-padding to eliminate the influence of the redundant information. Experimental results on IEEE CEC2017 and IEEE CEC2022 multitask benchmarks fully demonstrate the effectiveness of MMLMTO, which can significantly outperform other state-of-the-art multitask algorithms. Finally, MMLMTO is applied to a real-world multitask rover navigation application problem to further demonstrate its applicability.

Crowdsourcing contests have become an important method for individuals and organizations to solve complex problems by obtaining innovative solutions from external participants. As the number of participants continues to grow, the likelihood of undesirable outcomes increases, posing a great requirement for effective community detection algorithms. To provide platform owners with actionable and timely management strategies, this article proposes a multimodule-based dynamic community detection (MDCD) algorithm to facilitate the achievement of efficient, high-quality, and sustainable innovation. The MDCD algorithm uses a multimodule task learning framework containing four different modules, including heterogeneous temporal aggregation (HTA), representation reconstruction (RR), link prediction (LP), and node clustering (NC) modules, to gradually detect the community structure accurately. First, the HTA module obtains the initial node representation by capturing both spatial heterogeneity and temporal dependencies. Second, the RR module considers reconstructed topology and node attribute information to update the node representation via an encoder–decoder collaboration mechanism. Third, the LP module further optimizes the node representation by exploiting the predicted graph links, which helps increase the accuracy of community detection. Finally, the NC module leverages two metric learning methods to optimize a learnable clustering process based on the predicted node presentations, which helps platform owners achieve comprehensive results across multiple dimensions of innovation performance. The experimental results from real-world crowdsourcing platforms indicate that MDCD shows effectiveness in simultaneously improving the multidimensional innovation performance of crowdsourcing platforms and increasing solver engagement.

Evolutionary multitasking (EMT), as a very popular research topic in the evolutionary computation community, has been used to solve high-dimensional FS problems and has shown good performance recently. However, most of the existing EMT-based methods still have two drawbacks. First, they only consider using filter-based task generation strategies to retain highly relevant features for generating the additional tasks, whereas the redundancy between features is ignored. Second, they always consider a complete variable vector (e.g., global optimum or mean positional information of a population at current generation) as positive knowledge and transfer it, which greatly weakens the variety of transferred knowledge and increases the possibility of falling into local optimality. To deal with these two drawbacks, we propose a new EMT-assisted multitask particle swarm optimization (MPSO) algorithm with two innovations for high-dimensional FS. First, we propose a dual clustering-based task generation strategy to generate tasks by considering both feature relevance and redundancy. Second, we propose a fine-grain knowledge transfer strategy to realize explicit transfer of knowledge between different tasks. Experimental results on 15 public datasets show the effectiveness and competitiveness of our proposed MPSO algorithm over other state-of-the-art FS methods in dealing with high-dimensional FS problems.

Evolutionary multitask optimization (EMTO) has been increasingly employed in addressing high-dimensional feature selection challenges, but current EMTO algorithms still have three deficiencies: First, in task generation, they just consider the linear correlation among features but ignore the nonlinear correlation. Second, they commonly encounter negative knowledge transfer. Third, they are hard to strike an optimal balance between global search capability and computational efficiency. This paper proposes an adaptive aggregative multitask competitive swarm optimization (AAMCSO) for high-dimensional feature selection, which contains three novel and effective strategies to address the above deficiencies. Firstly, AAMCSO proposes a linear-and-nonlinear-correlation-based task generation strategy to generate multiple tasks while considering both linear and nonlinear correlation between features and labels. Secondly, AAMCSO proposes an adaptive aggregative knowledge transfer strategy to adaptively transfer positive knowledge among related tasks. Thirdly, AAMCSO proposes a bi-directional asymmetric flip strategy to guide the population to search for a smaller feature subset with better classification performance. We have conducted extensive comparative experiments on AAMCSO and multiple state-of-the-art feature selection algorithms in high-dimensional feature selection problems with up to 10000 dimensions. The results show that AAMCSO achieves significantly superior performance to the state-of-the-art comparison algorithms in terms of both classification accuracy and feature number.

Evolutionary multitask optimization is an emerging research topic that aims to solve multiple tasks simultaneously. A general challenge in solving multitask optimization problems (MTOPs) is how to effectively transfer common knowledge between/among tasks. However, knowledge transfer in existing algorithms generally has two limitations. First, knowledge is only transferred between the aligned dimensions of different tasks rather than between similar or related dimensions. Second, the knowledge transfer among the related dimensions belonging to the same task is ignored. To overcome these two limitations, this article proposes an interesting and efficient idea that divides individuals into multiple blocks and transfers knowledge at the block-level, called the block-level knowledge transfer (BLKT) framework. BLKT divides the individuals of all the tasks into multiple blocks to obtain a block-based population, where each block corresponds to several consecutive dimensions. Similar blocks coming from either the same task or different tasks are grouped into the same cluster to evolve. In this way, BLKT enables the transfer of knowledge between similar dimensions that are originally either aligned or unaligned or belong to either the same task or different tasks, which is more rational. Extensive experiments conducted on CEC17 and CEC22 MTOP benchmarks, a new and more challenging compositive MTOP test suite, and real-world MTOPs all show that the performance of BLKT-based differential evolution (BLKT-DE) is superior to the compared state-of-the-art algorithms. In addition, another interesting finding is that the BLKT-DE is also promising in solving single-task global optimization problems, achieving competitive performance with some state-of-the-art algorithms.

Multimodal optimization problems (MMOPs) have multiple global optima, hence the algorithm must preserve population diversity to locate multiple global optima and ensure the precision of the obtained solutions simultaneously. To achieve these, the niching technique is widely applied. Although the niching technique shows encouraging performance, some niches may continuously evolve even though accurate enough global optima in their regions have been found. This may cause the waste of computational resources and the inefficiency of search behavior. To maintain population diversity and accuracy, and to break through the mentioned deficiency, an optimal peaks detected-based differential evolution (OPPDE) algorithm is proposed, which has three novel components. Firstly, to maintain population diversity, OPDDE designs a parameter-insensitive OPTICS-based niching strategy to automatically partition niches. Secondly, to avoid wasting computation resources on founded global optima and enhance search efficiency, OPDDE designs an optimal peaks detection strategy that uses historical information to identify the founded global optima. Thirdly, a dynamic step local search strategy is used to refine solutions. The proposed OPDDE algorithm generally superiors some state-of-the-art algorithms regarding both the accuracy and completeness of solutions, according to experiments on widely used MMOP benchmarks.

Many real-world optimization problems require searching for multiple optimal solutions simultaneously, which are called multimodal optimization problems (MMOPs). For MMOPs, the algorithm is required both to enlarge population diversity for locating more global optima and to enhance refine ability for increasing the accuracy of the obtained solutions. Thus, numerous niching techniques have been proposed to divide the population into different niches, and each niche is responsible for searching on one or more peaks. However, it is often a challenge to distinguish proper individuals as niche centers in existing niching approaches, which has become a key issue for efficiently solving MMOPs. In this article, the niche center distinguish (NCD) problem is treated as an optimization problem and an NCD-based differential evolution (NCD-DE) algorithm is proposed. In NCD-DE, the niches are formed by using an internal genetic algorithm (GA) to online solve the NCD optimization problem. In the internal GA, a fitness-entropy measurement objective function is designed to evaluate whether a group of niche centers (i.e., encoded by a chromosome in the internal GA) is promising. Moreover, to enhance the exploration and exploitation abilities of NCD-DE in solving the MMOPs, a niching and global cooperative mutation strategy that uses both niche and population information is proposed to generate new individuals. The proposed NCD-DE is compared with some state-of-the-art and recent well-performing algorithms. The experimental results show that NCD-DE achieves better or competitive performance on both the accuracy and completeness of the solutions than the compared algorithms.

Many-task optimization problem is a kind of challenging multi-task optimization problem with more than three tasks. Two significant issues in solving many-task optimization problems are measuring inter-task similarity and transferring knowledge among similar tasks. However, most existing algorithms only use a single similarity measurement, which cannot accurately measure the inter-task similarity because the inter-task similarity is a concept with multiple different aspects. To address this limitation, this paper proposes a bi-objective knowledge transfer framework, which aims firstly to accurately measure different types of inter-task similarity using two different measurements and secondly to effectively transfer knowledge with different types of similarity via specific strategies. To achieve the first goal, a bi-objective measurement is designed to measure inter-task similarity from two different aspects, including shape similarity and domain similarity. To achieve the second goal, a similarity-based adaptive knowledge transfer strategy is designed to choose the suitable knowledge transfer strategy based on the type of inter-task similarity. We compare the bi-objective knowledge transfer framework-based algorithms with several state-of-the-art algorithms on two challenging many-task optimization test suites with 16 instances and on real-world many-task optimization problems with up to 500 tasks. The experimental results show that the proposed algorithms generally outperform the compared algorithms.

Multimodal optimization problems (MMOPs) are sorts of optimization problems that have many global optima. To discover as many peaks as possible and increase the accuracy of the solutions, MMOP requires algorithms with great exploration and exploitation abilities. However, exploration and exploitation are in an adversarial relationship, since exploration aims to locate more optima via searching the global space rather than small regions, whereas exploitation targets to enhance the accuracy of solutions via searching in small areas. The key to efficiently solving MMOPs lies in striking a balance between exploration and exploitation. To achieve the goal, this paper proposes an adversarial differential evolution (ADE), containing an adversarial reproduction strategy and an adversarial selection strategy. Firstly, adversarial reproduction strategy generates offspring for exploration and offspring for exploitation and lets these two types of offspring compete for survival. Secondly, adversarial selection strategy employs a diversity-optimization-based selection and a crowding-based selection to select the offspring with both good diversity and good fitness. Diversity-optimization-based selection transforms the problem of selecting diverse individuals into an optimizing problem and solves it via an extra genetic algorithm to get the offspring with optimal diversity. Extensive experiments are conducted on CEC2013 MMOP benchmark to verify the effectiveness and efficiency of the proposed ADE. Experimental results show that ADE has advantages over the state-of-the-art MMOP algorithms.

The berth allocation problem (BAP) is an NP-hard problem in maritime traffic scheduling that significantly influences the operational efficiency of the container terminal. This paper formulates the BAP as a permutation-based combinatorial optimization problem and proposes an improved ant colony system (ACS) algorithm to solve it. The proposed ACS has three main contributions. First, an adaptive heuristic information (AHI) mechanism is proposed to help ACS handle the discrete and real-time difficulties of BAP. Second, to relieve the computational burden, a divide-and-conquer strategy based on variable-range receding horizon control (vRHC) is designed to divide the complete BAP into a set of sub-BAPs. Third, a partial solution memory (PSM) mechanism is proposed to accelerate the ACS convergence process in each receding horizon (i.e., each sub-BAP). The proposed algorithm is termed as adaptive ACS (AACS) with vRHC strategy and PSM mechanism. The performance of the AACS is comprehensively tested on a set of test cases with different scales. Experimental results show that the effectiveness and robustness of AACS are generally better than the compared state-of-the-art algorithms, including the well-performing adaptive evolutionary algorithm and ant colony optimization algorithm. Moreover, comprehensive investigations are conducted to evaluate the influences of the AHI mechanism, the vRHC strategy, and the PSM mechanism on the performance of the AACS algorithm.