Short-ranged ordering for improved mean-field simulation of disordered media: insights from refractory-metal high-entropy alloy carbonitrides

Multi-principal element materials (MPEMs) have been attracting a rapidly growing interest due to their exceptional performance under extreme conditions, from cryogenic conditions to extreme-high temperatures and pressures. Despite the simple conceptual premise behind their formation, computational high-throughput first-principles design of such materials is extremely challenging due to the large number of realizations required for sufficient statistical sampling of their design space. Furthermore, MPEMs are also known to develop short-ranged orderings (SROs) which can play a significant role in their stability and properties. Here, we present an expedient and efficient first-principles computational framework for assessing the compositional and mechanical properties of MPEMs, including SRO effects. This heuristic methodology systematically corrects phase-averaged free-energies of MPEMs to include SRO phases, while imposing constraints for materials design. To illustrate the methodology, we study the stability and mechanical properties of equi-molar refractory-metal high-entropy alloy carbonitrides (RHEA-CNs) such as ZrNbMoHfTaWC3N3. We show that SRO, arising due to preferential neighboring among refractory metals, is necessary for thermodynamic and mechanical stability and to satisfy the imposed design criteria, leading to complex compositions for which their molar fraction and mechanical properties are predicted.Comment: 7 pages, 4 figures, and 1 table. Supplemental Material of 20 pages, 10 figures, and 4 tables. 17M

Self-adapting control parameters in particle swarm optimization

This study focuses on the development of a scheme for self-adapting the Particle Swarm Optimization (PSO) method to solve constrained optimization problems. PSO is a powerful nature-inspired metaheuristic optimization method. Compared to other methods, PSO has the ability to determine the optimal solution in fewer evaluations and in general performs in a more efficient and effective manner. However, researches show that the PSO method suffers from premature convergence and a dependence on the initial control settings. Due to these flaws, the application of PSO could lead to a failure in obtaining the global optimal solution. An extensive parametric sensitivity analysis was conducted to understand the impact of the individual control parameters and their respective influence on the performance of PSO. Results of the sensitivity analysis revealed that PSO was most sensitive to the inertia weight, cognitive component and social component. Modifications were performed on the original PSO algorithm to adapt the control parameters with respect to the circumstances of the particles at a specific moment. The modified PSO variant is called the Unique Adaptive Particle Swarm Optimization (UAPSO). Unique control parameters were established for each particle through using a novel term known as the evolutionary state. In the developed approach, constraints were handled by forcing the particles to learn from their personal feasible solutions only. Therefore, in the proposed method, the constraint handling technique worked in accord with the adapting scheme to ensure that the particles were adapting to the environment by directing itself to the feasible regions. Furthermore, particles were reinitialized whenever they stagnated in the design space. Verification of the performance of the proposed method was done by means of a comparative study with other well-known algorithms. The comparative study demonstrated that UAPSO proved to be effective and efficient in solving the considered problems and especially in terms of the speed of convergence. Furthermore, design of a three-bar truss was investigated through the application of UAPSO along with multiple variants of PSO. The numerical results showed the superiority of UAPSO compared to the other variants, its ability in avoiding premature convergence and its consistency and efficiency.Applied Science, Faculty ofMechanical Engineering, Department ofGraduat

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years