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Distributed pose graph optimization (DPGO) is one of the fundamental techniques of swarm robotics. Currently, the sub-problems of DPGO are built on the native poses. Our validation proves that this approach may introduce an imbalance in the…

This paper presents a sensitivity-based approach for the placement of distributed energy resources (DERs) in power systems. The approach is based on the fact that most planning studies utilize some form of optimization, and solutions to…

Distribution networks will experience more installations of distributed generation (DG) that is unpredictable and stochastic in nature. Greater distributed control and intelligence will allow challenges such as voltage control to be handled…

Traditional methods present a very restrictive range of applications, mainly limited by the features of the function to be optimized and of the constraint functions. In contrast, evolutionary algorithms present almost no restriction to the…

Cooperative communication using unmanned aerial vehicles (UAVs) is a promising technology for infrastructureless wireless networks. One of the key challenges in UAV based communications is the backhaul throughput. In this paper, we propose…

In this paper, we present a hybrid of Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) algorithms for numerically efficient global optimization of antenna arrays and metasurfaces. The hybrid EP-PSO algorithm uses an…

The dynamic of real-world optimization problems raises new challenges to the traditional particle swarm optimization (PSO). Responding to these challenges, the dynamic optimization has received considerable attention over the past decade.…

With the decline of fossil fuel reserves and the escalating global average temperature, the quest for environmentally friendly and renewable energy sources has gained significant momentum. Focus has turned to wind and photovoltaic energy,…

In this paper an on-line multiple faults detection approach is first of all proposed. For efficiency, an optimal design of membership functions is required. Thus, the proposed approach is improved using Particle Swarm Optimization (PSO)…

This paper derives, under minimal modelling assumptions, a simple to use theorem for obtaining both order-$1$ and order-$2$ stability criteria for a common class of particle swarm optimization (PSO) variants. Specifically, PSO variants that…

Nature-inspired swarm-based algorithms have been widely applied to tackle high-dimensional and complex optimization problems across many disciplines. They are general purpose optimization algorithms, easy to use and implement, flexible and…

We consider an optimization deployment problem of multistatic radar system (MSRS). Through the antenna placing and the transmitted power allocating, we optimally deploy the MSRS for two goals: 1) the first one is to improve the coverage…

Drones are effective for reducing human activity and interactions by performing tasks such as exploring and inspecting new environments, monitoring resources and delivering packages. Drones need a controller to maintain stability and to…

Unmanned aerial vehicles (UAVs) can serve as aerial base stations (BSs) to extend the ubiquitous connectivity for ground users (GUs) in the sixth-generation (6G) era. However, it is challenging to cooperatively deploy multiple UAV swarms in…

General purpose optimization routines such as nlminb, optim (R) or nlmixed (SAS) are frequently used to estimate model parameters in nonstandard distributions. This paper presents Particle Swarm Optimization (PSO), as an alternative to many…

We study a distributed framework for stochastic optimization which is inspired by models of collective motion found in nature (e.g., swarming) with mild communication requirements. Specifically, we analyze a scheme in which each one of $N >…

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located…

Remarkable penetration of renewable energy in electric networks, despite its valuable opportunities, such as power loss reduction and loadability improvements, has raised concerns for system operators. Such huge penetration can lead to a…

We propose a GPU-accelerated distributed optimization algorithm for controlling multi-phase optimal power flow in active distribution systems with dynamically changing topologies. To handle varying network configurations and enable…

The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm for solving combinatorial optimization problems such as the Max Cut problem. A key challenge in QAOA is the efficient identification of variational…