Fei Ming

Evolutionary multitasking (EMT) has emerged as a popular topic of evolutionary computation over the past decade. It aims to concurrently address multiple optimization tasks within limited computing resources, leveraging inter-task knowledge transfer techniques. Despite the abundance of multitask evolutionary algorithms (MTEAs) proposed for multitask optimization (MTO), there remains a need for a comprehensive software platform to help researchers evaluate MTEA performance on benchmark MTO problems as well as explore real-world applications. To bridge this gap, we introduce the first open-source benchmarking platform, named MToP, for EMT. MToP incorporates over 50 MTEAs, more than 200 MTO problem cases with real-world applications, and over 20 performance metrics. Based on these, we provide benchmarking recommendations tailored for different MTO scenarios. Moreover, to facilitate comparative analyses between MTEAs and traditional evolutionary algorithms, we adapted over 50 popular single-task evolutionary algorithms to address MTO problems. Notably, we release extensive pre-run experimental data on benchmark suites to enhance reproducibility and reduce computational overhead for researchers. MToP features a user-friendly graphical interface, facilitating results analysis, data export, and schematic visualization. More importantly, MToP is designed with extensibility in mind, allowing users to develop new algorithms and tackle emerging problem domains. The source code of MToP is available at: https://github.com/intLyc/MTO-Platform

Neural architecture search (NAS) has garnered significant attention and achieved remarkable success in deep learning. In recent years, the increasing demand for generic, resource-available, and trustworthy AI has shifted research focus beyond mere accuracy to model complexity, energy efficiency, and inference latency, leading to the emergence of multi-objective neural architecture search problems (MONASPs). However, existing methods often integrate classical multi-objective evolutionary algorithms (MOEAs) as the search strategy with problem-specific strategies, limiting their applicability across different deep neural network models and deep learning tasks. Therefore, there is a growing need for MOEA-based search strategies that can accommodate the characteristics of MONASPs. This work proposes an evolutionary framework as a search strategy for MONASPs. Specifically, a coevolutionary mechanism is proposed where an auxiliary population prioritizes decision space diversity to improve the search for multi-modal landscapes. Moreover, a two-stage offspring generation mechanism is devised. The first stage exploits the advantage of a crossover operator to enhance convergence towards the optimal regions in the multi-modal landscape. Then, the second stage adopts a differential evolution operator to manage potential linkages between decision variables, promoting the exploration of diverse architectures along the Pareto front. The effectiveness of this new framework is validated through comparisons with eight representative MOEAs and 22 advanced NAS methods. Experiments are conducted across seven search spaces, utilizing two large common datasets, and assessed by four performance indicators. The results demonstrate the competitiveness and superiority of the framework in providing practitioners with diverse and high-performance architectures.

Learning to optimize and automated algorithm design are attracting increasing attention, but it is still in its infancy in constrained multiobjective optimization evolutionary algorithms (CMOEAs). Current learning-assisted CMOEAs are typically crafted by human experts using manually designed techniques, which tend to be overly tuned, ad hoc, and lacking versatility. To alleviate these limitations, this work proposes transforming the online configuration of CMOEA into determinations of discrete and continuous parameters, which are then solved by deep reinforcement learning (DRL) techniques. Specifically, the Actor–Critic framework is adapted to determine a factor that defines the environmental selection pressure. The deep Q-learning technique is adopted to determine the operators for producing offspring. Owing to the property of DRL, the configured algorithm can accommodate historical experience, current evolutionary dynamics, and future improvements to achieve self-learning. A new CMOEA is proposed using the automatically configured EA. Experiments on four challenging benchmarks and 21 real-world problems verify that our method significantly outperforms 11 state-of-the-art methods. The versatility and superiority of the automatically configured environment and operators over handcrafted methods justify the effectiveness of the automated configuration method, demonstrating a promising direction in evolutionary multiobjective optimization.

Achieving a trade-off between convergence, feasibility, and diversity is critical for solving constrained multiobjective optimization problems (CMOPs). Existing constrained multiobjective evolutionary algorithms (CMOEAs) primarily focus on constraint-handling techniques to balance constraint satisfaction and objective optimization. However, individual diversity is generally considered to be low. Owing to the insufficient enhancement of diversity, CMOEAs are unable to disperse well in the objective space to enhance the search for the constrained Pareto front (CPF) when handling CMOPs with complex constraints. To address this limitation, this study develops a diversity-enhanced tri-stage framework with three different evolutionary stages. First, sufficient convergence is enabled to move the population across the infeasible regions. Afterward, an angle-domination strategy is designed, aiming to spread the population evenly in the objective space while maintaining the achieved convergence. Third, we propose a minimum neighborhood-based domination strategy to ensure that the population searches the CPF by pursuing an even distribution in the objective space. Moreover, a weight vector pre-selection strategy is proposed to reduce computational overhead by avoiding ineffective searches in regions that do not include the CPF. Extensive experiments with 48 benchmark instances and 25 real-world instances validate the effectiveness of our approach over nine state-of-the-art methods.

Since most multi-objective optimization problems in real-world applications contain constraints, constraint-handling techniques (CHTs) are necessary for a multi-objective optimizer. However, existing CHTs give no relaxation to objectives, resulting in the elimination of infeasible dominated solutions that are promising (potentially useful but inferior) for detecting feasible regions and the constrained Pareto front (CPF). To overcome this drawback, in this work, we propose an objective relaxation technique that can preserve promising by relaxing objective function values, i.e., convergence, through an adaptively adjusted relaxation factor. Further, we develop a new constrained multiobjective optimization evolutionary algorithm (CMOEA) based on relaxations on both constraints and objectives. The proposed algorithm evolves one population by the constraint relaxation technique to preserve promising infeasible solutions and the other population by both objective and constraint relaxation techniques to preserve promising infeasible dominated solutions. In this way, our method can overcome the drawback of existing CHTs. Besides, an archive update strategy is designed to maintain encountered feasible solutions by the two populations to approximate the CPF. Experiments on challenging benchmark problems and real-world problems have demonstrated the superiority or at least competitiveness of our proposed CMOEA. Moreover, to verify the generality of the objective relaxation technique, we embed it into two existing CMOEA frameworks and the results show that it can significantly improve the performance in handling challenging problems.

The decomposable feature of operations in the welding shop scheduling scenario results in a vast search space, posing challenges for the design of traditional optimization algorithms. Addressing the multi-objective distributed heterogeneous welding shop scheduling problem (DHWSP), this work introduces a generalized multitasking framework. It establishes an auxiliary task by employing knowledge-and-learning-synergy neighborhood search, thereby enhancing the convergence and diversity of the original task. In this framework, an enhanced competitive swarm optimizer is adopted as the original task for DHWSP. Additionally, knowledge expression and transfer strategies are designed to expedite the comprehensive performance of each task by leveraging knowledge gained from search results. Finally, a memetic algorithm based on the multitasking framework is proposed for DHWSP. The effectiveness of the algorithm is validated through extensive experiments on 20 DHWSP instances. Numerical experimental results indicate that the proposed multitasking framework can significantly improve algorithmic comprehensive performance, demonstrating its efficacy in addressing the multi-objective DHWSP within a complex search space.

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually difficult to select suitable operators for the problem at hand. Hence, improving operator selection is promising and necessary for CMOEAs. This work proposes an online operator selection framework assisted by Deep Reinforcement Learning. The dynamics of the population, including convergence, diversity, and feasibility, are regarded as the state; the candidate operators are considered as actions; and the improvement of the population state is treated as the reward. By using a Q-network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance. The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems. The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.