Recently, Distributed Constraint optimization Problems (DCOP) have been drawing a growing body of attention as an important research area in multi agent systems as a large body of real problems can be modeled by them. The primary goal of this research is to design a Distributed and effective algorithm to solve DCOP. There are various criteria that measure the efficiency of DCOP algorithms, but the most efficient algorithm for DCOP is the one by which the computation and communication cost is as low as possible and the quality of the solution is high. In this paper, we focus on an approximate DCOP algorithm called DALO (Distributed Asynchronous Local optimization). Using the main idea of the DALO algorithm, we propose a new algorithm to solve DCOP, which exhibits two important improvements over the DALO algorithm. First we use a sequential partial approach to select a coefficient of leaders to compute the best assignment for agents by which the computation and communication cost decrease in the whole DCOP. The second improvement is an evolutionary approach by which the computation and communication burden for each agent decreases. We present some empirical evidences that show our algorithm performs better than the DALO algorithm.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex optimization problem that guarantees the global optimal solution. The convex optimization problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

This paper investigates a novel method to solve Distributed optimization problems in the presence of communication delays between the networked agents that cooperate together to find an optimal solution of a global cost function composed of local ones. In the problem of Distributed optimization in a network of multi-agent because of existing phenomena such as communication delay, deriving approaches having appropriate performance so that the states of all agents converge to the same value always has been a substantial challenge. Delay-dependent conditions in the form of linear matrix inequities are derived to analyze the convergence of the introduced scheme to the optimal solution. It is demonstrated that the maximum allowable time delay in the network and convergence rate of the optimization procedure are increased by the suggested strategy. Finally, comparative simulation results are considered to illustrate the superior performance of the introduced scheme compared to a rival one in the literature.

The Osprey optimization Algorithm (OOA), an emerging metaheuristic method, has recently gained significant attention in the field of Distributed systems. Inspired by the predatory behavior of ospreys, the algorithm exhibits a strong capability to balance global exploration with local exploitation, thereby offering effective solutions for complex and multi-objective optimization problems. Over the past decade, Distributed systems have become the backbone of modern technologies, enabling seamless cooperation among geographically dispersed components and supporting large-scale data processing and management. This article studies research on OOA applications in Distributed systems. Nineteen selected studies were analyzed, covering domains such as the Internet of Things, cloud and fog computing, smart grids, microgrids, wireless sensor networks, and smart healthcare systems. The findings reveal that OOA can significantly reduce energy consumption, improve resource allocation, enhance data security, and increase the efficiency and resilience of Distributed systems. Nevertheless, challenges remain, including computational complexity, sensitivity to parameter tuning, and the lack of real-world experimental validations. The results of this review highlight promising avenues for developing enhanced variants of the algorithm and extending its deployment in practical operational environments.