Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it still suffers from poor solution accuracy and premature convergence when addressing some complex cases due to the insufficient exploitation phase. In contrast, ARO possesses very competitive exploitation potential, but its exploration ability needs to be more satisfactory. To ameliorate the above-mentioned limitations in a single algorithm and achieve better overall optimization performance, this paper proposes a novel chaotic opposition-based learning-driven hybrid AO and ARO algorithm called CHAOARO. Firstly, the global exploration phase of AO is combined with the local exploitation phase of ARO to maintain the respective valuable search capabilities. Then, an adaptive switching mechanism (ASM) is designed to better balance the exploration and exploitation procedures. Finally, we introduce the chaotic opposition-based learning (COBL) strategy to avoid the algorithm fall into the local optima. To comprehensively verify the effectiveness and superiority of the proposed work, CHAOARO is compared with the original AO, ARO, and several state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Systematic comparisons demonstrate that CHAOARO can significantly outperform other competitor methods in terms of solution accuracy, convergence speed, and robustness. Furthermore, the promising prospect of CHAOARO in real-world applications is highlighted by resolving five industrial engineering design problems and photovoltaic (PV) model parameter identification problem.

The range of selective laser sintering (SLS) materials is currently limited, and the available materials are often of high cost. Moreover, the mechanical strength of wood–plastic SLS parts is low, which restricts the application of a SLS technology. A new composite material has been proposed to address these issues, while simultaneously valorizing agricultural and forestry waste. This composite presents several advantages, including reduced pollution associated with waste disposal and reduced CO₂ emission with the SLS process in addition to good mechanical strength. In this article, a novel and low-cost Prosopis chilensis/polyethersulfone composite (PCPC) was used as a primary material for SLS. The formability of PCPC with various raw material ratios was investigated via single-layer experiments, while the mechanical properties and dimensional accuracy of the parts produced using the various PCPC ratios were evaluated. Further, the microstructure and particle distribution in the PCPC pieces were examined using scanning electron microscopy. The result showed that the SLS part produced via 10/90 (wt/wt) PCPC exhibited the best mechanical strength and forming quality compared to other ratios and pure polyethersulfone (PES), where bending and tensile strengths of 10.78 and 4.94 MPa were measured. To improve the mechanical strength, post-processing infiltration was used and the PCPC-waxed parts were enhanced to 12.38 MPa and 5.73 MPa for bending and tensile strength.

Current wood-plastic materials available for selective laser sintering (SLS) are limited and often suffer from low-quality and mechanical strength. In this study, a new composite of peanut husk powder (PHP)/polyether sulfone (PES) was developed for SLS additive manufacturing (AM). To use the biomass waste materials in AM technology, such as furniture and wood flooring, this composite based on agricultural waste is environmentally friendly, energy efficient, and low in production cost. SLS parts made from PHPC had good mechanical strength and excellent dimensional precision (DP). The thermal decomposition temperature of composite powder components and the glass transition temperatures of PES and various PHPC were determined first to prevent the PHPC parts from warping during sintering. Furthermore, the formability of PHPC powders in various mixing ratios was examined through single-layer sintering; and the density, mechanical strength, surface roughness, and DP of the sintered parts were measured. Particle distribution and microstructure of the powders and the SLS parts (both before and after breakage in mechanical tests) were inspected using scanning electron microscopy. According to the combined results, a ratio of PHP/PES = 10/90 (w/w) resulted in the best forming quality and mechanical strength compared with other ratios and pure PES. The measured density, impact strength, tensile strength, and bending strength for this PHPC are 1.1825 g/cm ³ , 2.12 kJ/cm ² , 6.076 MPa, and 14.1 MPa, respectively. After wax infiltration, these parameters were further improved to 2.0625 g/cm ³ , 2.96 kJ/cm ² , 7.476 MPa, and 15.7 MPa, respectively.

In this paper, a biomimetic flexible fishtail actuated by shape memory alloy (SMA) wires is presented. The detailed structure of the fishtail propeller is designed to mimic the muscle and bone structure of a Carassius auratus. The mechanical modeling of the biomimetic fishtail is built; then, constitutive equations and thermal models of embedded SMA wires are described. Next, experiments are carried out to analyze the propulsive performance of the fishtail. Its maximum bending angle is 50° under the conditions of a heating voltage of 14.8 V and a heating time of 175 ms at a water temperature of 24.5°C. The symmetrical characteristic of both sides of the biomimetic fishtail is good, and different fluctuation can be adjusted by changing the heating time. The bending time and recovery time are recorded, and the effect of heating time on oscillation frequency is analyzed. Finally, the propulsive forces of three different heating modes at frequencies of 0.4 Hz, 0.5 Hz and 0.6 Hz are tested and compared. A relatively good heating mode is discovered for controlling the biomimetic fishtail during the experiments. The biomimetic fishtail obtains the largest average propulsive force of 0.041 N at a frequency of 0.8 Hz with a heating time of 80 ms and a bending angle of 22°.

Harris Hawks Optimization (HHO) is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems. Nevertheless, the basic HHO algorithm still has certain limitations, including the tendency to fall into the local optima and poor convergence accuracy. Coot Bird Optimization (CBO) is another new swarm-based optimization algorithm. CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface. Although the framework of CBO is slightly complicated, it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions. This paper proposes a novel enhanced hybrid algorithm based on the basic HHO and CBO named Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization (EHHOCBO). EHHOCBO can provide higher-quality solutions for numerical optimization problems. It first embeds the leadership mechanism of CBO into the population initialization process of HHO. This way can take full advantage of the valuable solution information to provide a good foundation for the global search of the hybrid algorithm. Secondly, the Ensemble Mutation Strategy (EMS) is introduced to generate the mutant candidate positions for consideration, further improving the hybrid algorithm’s exploration trend and population diversity. To further reduce the likelihood of falling into the local optima and speed up the convergence, Refracted Opposition-Based Learning (ROBL) is adopted to update the current optimal solution in the swarm. Using 23 classical benchmark functions and the IEEE CEC2017 test suite, the performance of the proposed EHHOCBO is comprehensively evaluated and compared with eight other basic meta-heuristic algorithms and six improved variants. Experimental results show that EHHOCBO can achieve better solution accuracy, faster convergence speed, and a more robust ability to jump out of local optima than other advanced optimizers in most test cases. Finally, EHHOCBO is applied to address four engineering design problems. Our findings indicate that the proposed method also provides satisfactory performance regarding the convergence accuracy of the optimal global solution.