Remora Optimization Algorithm Combining Joint Opposite Selection and Host Switching

The remora optimization algorithm (ROA) is a meta heuristic optimization algorithm proposed in 2021. It simulates the behavior of parasitic attachment to the host, empirical attack and host foraging in the ocean. The structure of ROA is simple and easy to implement, but the overall situation is slightly insufficient, which easily leads to ROA’s slow convergence speed and even difficult convergence in the later period. To solve the above problems, host switching mechanism is added in the exploration phase, and new host beluga is introduced to improve the exploration ability of original ROA. At the same time, through adding joint opposite selection strategy, the ability of the algorithm to jump out of the local optimum is enhanced, and the comprehensive optimization performance of the algorithm is further improved. Through the above improvements, an improved remora optim-ization algorithm (IROA) is proposed, which integrates the joint opposite selection and host switching mechanism. In order to verify the performance and improvement advantages of IROA, IROA is compared with the original ROA, six typical original algorithms and four improved algorithms on ROA. Experimental results of CEC2020 standard test function show that IROA has stronger optimization ability and higher convergence accuracy. Finally, the advantages and engineering applicability of the improved algorithm are further verified by solving the car crashworthiness design problem

Formation criterion for binary metal diboride solid solutions established through combinatorial methods

Establishing the formation criterion is urgent for accelerating the discovery and design of solid-solution materials with desirable properties. The previously reported formation criterion mainly focused on solid-solution alloys, while the formation criterion was rarely established in solid-solution ceramics. To solve this problem, herein, we take a class of solid-solution ceramics, namely binary metal diboride ((MxN1-x)B-2) solid solutions, as a prototype. Through combinatorial methods including high-throughput molten salt syntheses and high-throughput first-principles calculations combined with the machine learning approach, the correlation between influential factors, including atomic size difference (delta), mixing enthalpy at 0 K and 0 Pa (Delta Hmix0K), doping condition (phi), and valence electron concentration (VEC), and the formation ability of (MxN1-x)B-2 solid solutions was first studied systematically, and then their formation criterion was well established. The results showed that the influential degree of the aforementioned four factors on the formation ability of (MxN1-x)B-2 solid solutions could be described as follows: delta \u3e Delta Hmix0K\u3e phi \u3e VEC. In addition, a newly proposed parameter, beta, could well reflect the formation ability of (MxN1-x)B-2 solid solutions: when beta \u3e 0, the single-phase (MxN1-x)B-2 solid solutions could be successfully synthesized in our work and vice versa. This study may provide a theoretical guidance in the discovery and design of various solid-solution ceramics, such as the metal borides, carbides, nitrides, etc, with desirable properties

An improved multi-strategy beluga whale optimization for global optimization problems

This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems

Heptacarbonylbis(μ-propane-1,3-dithiolato)triiron(I,II)(2 Fe—Fe)

The trinuclear title compound, [Fe3(C3H6S2)2(CO)7], is a mixed-valent FeI/FeII complex and crystallizes with two molecules of similar configuration in the asymmetric unit. The three Fe atoms in each molecule display a bent arrangement [Fe—Fe—Fe = 156.22 (4) and 157.06 (3)°]. Both outer FeI atoms are six-coordinated in a distorted ocahedral coordination geometry defined by the bridging FeII atom, three carbonyl C atoms and two bridging S atoms. The coordination number of the central FeII atom is seven and includes bonding to the two outer FeI atoms, four bridging S atoms and one carbonyl C atom. The resulting coordination polyhedron might be described as a highly distorted monocapped trigonal prism. In the crystal packing, the molecules exhibit a chain-like arrangement parallel to [100] and [001], and the resulting layers are stacked along [010]. The cohesion of the structure is dominated by van der Waals interactions

Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems

The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic optimization algorithm. It stimulates the hunting behavior of the sand cat, which attacks or searches for prey according to the sound frequency; each sand cat aims to catch better prey. Therefore, the sand cat will search for a better location to catch better prey. In the SCSO algorithm, each sand cat will gradually approach its prey, which makes the algorithm a strong exploitation ability. However, in the later stage of the SCSO algorithm, each sand cat is prone to fall into the local optimum, making it unable to find a better position. In order to improve the mobility of the sand cat and the exploration ability of the algorithm. In this paper, a modified sand cat swarm optimization (MSCSO) algorithm is proposed. The MSCSO algorithm adds a wandering strategy. When attacking or searching for prey, the sand cat will walk to find a better position. The MSCSO algorithm with a wandering strategy enhances the mobility of the sand cat and makes the algorithm have stronger global exploration ability. After that, the lens opposition-based learning strategy is added to enhance the global property of the algorithm so that the algorithm can converge faster. To evaluate the optimization effect of the MSCSO algorithm, we used 23 standard benchmark functions and CEC2014 benchmark functions to evaluate the optimization performance of the MSCSO algorithm. In the experiment, we analyzed the data statistics, convergence curve, Wilcoxon rank sum test, and box graph. Experiments show that the MSCSO algorithm with a walking strategy and a lens position-based learning strategy had a stronger exploration ability. Finally, the MSCSO algorithm was used to test seven engineering problems, which also verified the engineering practicability of the proposed algorithm

Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems

The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic optimization algorithm. It stimulates the hunting behavior of the sand cat, which attacks or searches for prey according to the sound frequency; each sand cat aims to catch better prey. Therefore, the sand cat will search for a better location to catch better prey. In the SCSO algorithm, each sand cat will gradually approach its prey, which makes the algorithm a strong exploitation ability. However, in the later stage of the SCSO algorithm, each sand cat is prone to fall into the local optimum, making it unable to find a better position. In order to improve the mobility of the sand cat and the exploration ability of the algorithm. In this paper, a modified sand cat swarm optimization (MSCSO) algorithm is proposed. The MSCSO algorithm adds a wandering strategy. When attacking or searching for prey, the sand cat will walk to find a better position. The MSCSO algorithm with a wandering strategy enhances the mobility of the sand cat and makes the algorithm have stronger global exploration ability. After that, the lens opposition-based learning strategy is added to enhance the global property of the algorithm so that the algorithm can converge faster. To evaluate the optimization effect of the MSCSO algorithm, we used 23 standard benchmark functions and CEC2014 benchmark functions to evaluate the optimization performance of the MSCSO algorithm. In the experiment, we analyzed the data statistics, convergence curve, Wilcoxon rank sum test, and box graph. Experiments show that the MSCSO algorithm with a walking strategy and a lens position-based learning strategy had a stronger exploration ability. Finally, the MSCSO algorithm was used to test seven engineering problems, which also verified the engineering practicability of the proposed algorithm

Formation criterion for binary metal diboride solid solutions established through combinatorial methods

Establishing the formation criterion is urgent for accelerating the discovery and design of solid-solution materials with desirable properties. The previously reported formation criterion mainly focused on solid-solution alloys, while the formation criterion was rarely established in solid-solution ceramics. To solve this problem, herein, we take a class of solid-solution ceramics, namely binary metal diboride ((MxN1-x)B-2) solid solutions, as a prototype. Through combinatorial methods including high-throughput molten salt syntheses and high-throughput first-principles calculations combined with the machine learning approach, the correlation between influential factors, including atomic size difference (delta), mixing enthalpy at 0 K and 0 Pa (Delta Hmix0K), doping condition (phi), and valence electron concentration (VEC), and the formation ability of (MxN1-x)B-2 solid solutions was first studied systematically, and then their formation criterion was well established. The results showed that the influential degree of the aforementioned four factors on the formation ability of (MxN1-x)B-2 solid solutions could be described as follows: delta > Delta Hmix0K> phi > VEC. In addition, a newly proposed parameter, beta, could well reflect the formation ability of (MxN1-x)B-2 solid solutions: when beta > 0, the single-phase (MxN1-x)B-2 solid solutions could be successfully synthesized in our work and vice versa. This study may provide a theoretical guidance in the discovery and design of various solid-solution ceramics, such as the metal borides, carbides, nitrides, etc, with desirable properties.</p

Analytical energy spectrum for hybrid mechanical systems

We investigate the energy spectrum for hybrid mechanical systems described by non-parity-symmetric quantum Rabi models. A set of analytical solutions in terms of the confluent Heun functions and their analytical energy spectrum is obtained. The analytical energy spectrum includes regular and exceptional parts, which are both confirmed by direct numerical simulation. The regular part is determined by the zeros of the Wronskian for a pair of analytical solutions. The exceptional part is relevant to the isolated exact solutions and its energy eigenvalues are obtained by analyzing the truncation conditions for the confluent Heun functions. By analyzing the energy eigenvalues for exceptional points, we obtain the analytical conditions for the energy-level crossings, which correspond to two-fold energy degeneracy