Density functional theory calculations have been performed to determine the relationship between structures and reactivity of anatase and brookite TiO2 surfaces. Brookite TiO2(210) has the same structural building block of anatase TiO2(101), but interatomic distances are slightly shorter and the blocks are arranged in a different way. Our calculations show that these differences significantly change the reactivity toward adsorption of various molecules, and most importantly, generate highly active sites at the junction between different structural units on brookite TiO2(210). These results suggest that brookite TiO2(210) would exhibit distinct activity, which may be useful in catalytic and photocatalytic applications.

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Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.

Fish-inspired motion is an important research area with many applications in real-world tasks such as underwater vehicles or robotic fish control design. Owing to robust, smooth, and coordinated oscillatory signals generated by Central Pattern Generators (CPGs) for locomotion control of robots with multiple degrees of freedom, CPGs are the most versatile solution for robotic control systems, especially in robotic fish. However, tuning central pattern generator parameters is difficult for complex mechanical system designs. Besides, most current CPG-based methods only consider one aspect (e.g., speed), which widens the gap between theory and practice in robotic fish design. Also, it may affect the practical applicability of the designed motion model to a certain extent. This paper addresses this problem by constructing a multi-objective evolutionary design of a central pattern generator network to control the proposed biomimetic robotic fish. A new CPG model is proposed to help biomimetic robotic fish swim efficiently. In addition, an efficient multi-objective evolutionary algorithm proposed in our previous work is also applied to assist the biomimetic robotic fish in obtaining faster-swimming speed, good stability of the head, and higher propulsive efficiency simultaneously. Considering that the result of multi-objective optimization is a set of non-dominated solutions rather than a solution, a screening method based on fuzzy theory is adopted to assist decision-makers in selecting the most appropriate solution. Based on this, the control model of biomimetic robotic fish is constructed. The proposed control model is simulated and compared with seven well-known algorithms and a series of robotic fish designs. After that, the proposed control model is validated with extensive experiments on the actual biomimetic robotic fish. Simulations and experiments demonstrate the proposed control model’s effectiveness and good performance, especially when the control model has been applied to the real biomimetic robotic fish.

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Compared with traditional underwater vehicles, bio-inspired fish robots have the advantages of high efficiency, high maneuverability, low noise, and minor fluid disturbance. Therefore, they have gained an increasing research interest, which has led to a great deal of remarkable progress theoretically and practically in recent years. In this review, we first highlight our enhanced scientific understanding of bio-inspired propulsion and sensing underwater and then present the research progress and performance characteristics of different bio-inspired robot fish, classified by the propulsion method. Like the natural fish species they imitate, different types of bionic fish have different morphological structures and distinctive hydrodynamic properties. In addition, we select two pioneering directions about soft robotic control and multi-phase robotics. The hybrid dynamic control of soft robotic systems combines the accuracy of model-based control and the efficiency of model-free control, and is considered the proper way to optimize the classical control model with the intersection of multiple machine learning algorithms. Multi-phase robots provide a broader scope of application compared to ordinary bionic robot fish, with the ability of operating in air or on land outside the fluid. By introducing recent progress in related fields, we summarize the advantages and challenges of soft robotic control and multi-phase robotics, guiding the further development of bionic aquatic robots.

Balancing convergence and diversity has become a key point especially in many-objective optimization where the large numbers of objectives pose many challenges to the evolutionary algorithms. In this paper, an opposition-based evolutionary algorithm with the adaptive clustering mechanism is proposed for solving the complex optimization problem. In particular, opposition-based learning is integrated in the proposed algorithm to initialize the solution, and the nondominated sorting scheme with a new adaptive clustering mechanism is adopted in the environmental selection phase to ensure both convergence and diversity. The proposed method is compared with other nine evolutionary algorithms on a number of test problems with up to fifteen objectives, which verify the best performance of the proposed algorithm. Also, the algorithm is applied to a variety of multiobjective engineering optimization problems. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in solving challenging real-world problems.