Constrained multi-objective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs with small feasible regions. To remedy this issue, this paper proposes a coevolutionary framework for constrained multi-objective optimization, which solves a complex CMOP assisted by a simple helper problem. The proposed framework evolves one population to solve the original CMOP and evolves another population to solve a helper problem derived from the original one. While the two populations are evolved by the same optimizer separately, the assistance in solving the original CMOP is achieved by sharing useful information between the two populations. In the experiments, the proposed framework is compared to several state-of-the-art algorithms tailored for CMOPs. High competitiveness of the proposed framework is demonstrated by applying it to 47 benchmark CMOPs and the vehicle routing problem with time windows.

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Outsourcing logistics operation to third-party logistics has attracted more attention in the past several years. However, very few papers analyzed fuel consumption model in the context of outsourcing logistics. This problem involves more complexity than traditional open vehicle routing problem (OVRP), because the calculation of fuel emissions depends on many factors, such as the speed of vehicles, the road angle, the total load, the engine friction, and the engine displacement. Our paper proposed a green open vehicle routing problem (GOVRP) model with fuel consumption constraints for outsourcing logistics operations. Moreover, a hybrid tabu search algorithm was presented to deal with this problem. Experiments were conducted on instances based on realistic road data of Beijing, China, considering that outsourcing logistics plays an increasingly important role in China's freight transportation. Open routes were compared with closed routes through statistical analysis of the cost components. Compared with closed routes, open routes reduce the total cost by 18.5% with the fuel emissions cost down by nearly 29.1% and the diver cost down by 13.8%. The effect of different vehicle types was also studied. Over all the 60-and 120-node instances, the mean total cost by using the light-duty vehicles is the lowest.

Visible‐to‐near‐infrared organic photodetectors (vis–NIR OPDs) are highly desired due to their potential applications in both scientific research and industry. To develop state‐of‐the‐art diode‐type OPDs, general strategies aiming to reduce the dark current density (Jd) involve reducing the thickness of the bulk‐heterojunction (BHJ) active layer, but this simultaneously leads to a sharp drop in photoresponse. Herein, a facile fabrication strategy, i.e., thermally induced anti‐aggregation (TIAA) evolution strategy, is introduced for manipulating the phase separation scale and molecular arrangement orientation of the PBDB‐T:Y6 based active layer. To a typical 500 nm‐thick BHJ, much higher and balanced electron/hole mobilities are achieved through the TIAA manipulation. By effectively equilibrating the Jd (8.7 × 10‐8 A cm‐2) and responsibility (0.5 A W‐1 at 860 nm), the optimized vis–NIR OPD showcases high specific detection of over 1012 Jones (380–940 nm) and capability of faint IR light detection (≈10‐10 W cm‐2 at 850 nm) at −0.5 V bias. Meanwhile, the device displays the ability to monitor pulse signal in real time through photoplethysmography, indicating the potential of TIAA evolution strategy to fabricate high‐performance vis–NIR OPDs for next‐generation wearable health monitoring.